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Applications of Machine Learning and Big Data Analytics for Environmental Sustainability

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 104006

Special Issue Editors

Dr. Saqib Iqbal Hakak

E-Mail Website
Guest Editor
Canadian Instititute for Cybersecurity, Faculty of Computer Science, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
Interests: cybersecurity; natural language processing; edge Computing and Applications of AI
Dr. Thippa Reddy Gadekallu

E-Mail Website
Guest Editor
School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu 632014, India
Interests: machine learning; computer vision; blockchain; deep neural networks; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the advancements in technologies such as Internet of Things, social media, high-resolution remote sensing techniques and advanced communication techniques, there are now abundant data related to the environment. The environmental data include weather data, remote sensing from satellites and data related to pollution, natural calamities and water management. Analyzing the patterns from these data may help the responsible agencies (such as government agencies, NGOs, etc.)  to make appropriate decisions to save lives and property. However, the volume, velocity and complexity of the generated data make it difficult to extract useful information from the data using traditional machine learning algorithms.

Big data techniques can be used to effectively handle and process the complexities in environmental data such as volume, heterogeneity and velocity.  Integrating machine learning with big data can help us in understanding patterns from the environmental data, which can be used to understand the patterns in these data and can predict natural calamities/disasters well in advance so that the damages incurred can be minimized. Some of the applications of integrating big data with machine learning include the prediction of natural disasters (e.g., floods, cyclones, earthquakes), the prediction of rainfall, the prediction of pollution levels, the recognition of biodiversity in acoustic images and remote sensing of the environment.

The aim of this Special Issue is to solicit state-of-the-art research on current environmental issues. Experimental results, survey/review papers and case studies are also accepted. Potential topics of this Special Issue include, but are not limited to, applications of machine learning and big data with big data for a sustainable environment, such as:

  • Weather forecasting;
  • Disaster management;
  • Precision agriculture;
  • Water management;
  • Industrial wastewater management;
  • Federated learning for environmental monitoring;
  • Blockchain for environmental monitoring;
  • Pollution control;
  • Unmanned aerial communication-based environmental monitoring.

Dr. Saqib Iqbal Hakak
Prof. Dr. Thippa Reddy Gadekallu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data analytics
  • artificial intelligence
  • environmental monitoring
  • big data analytics

Published Papers (36 papers)

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19 pages, 4292 KiB  
Article
The Adoption of a Big Data Approach Using Machine Learning to Predict Bidding Behavior in Procurement Management for a Construction Project
by Wuttipong Kusonkhum, Korb Srinavin and Tanayut Chaitongrat
Sustainability 2023, 15(17), 12836; https://doi.org/10.3390/su151712836 - 24 Aug 2023
Cited by 1 | Viewed by 1237
Abstract
Big data technologies are disruptive technologies that affect every business, including those in the construction industry. The Thai government has also been affected and attempted to use machine learning techniques with the analytics of big data technologies to predict which construction projects have [...] Read more.
Big data technologies are disruptive technologies that affect every business, including those in the construction industry. The Thai government has also been affected and attempted to use machine learning techniques with the analytics of big data technologies to predict which construction projects have a winning price over the project budget. However, this technology was never developed, and the government did not implement it because they had data obtained via a traditional data collection process. In this study, traditional data were processed to predict the behavior in Thai government construction projects using a machine learning model. The data were collected from the government procurement system in 2019. There were seven input data, including the project owner department, type of construction project, bidding method, project duration, project scale, winning price overestimated price, and winning price over budget. A range of classification techniques, including an artificial neural network (ANN), a decision tree (DC), and a K-nearest neighbor (KNN), were used in this study. According to the results, after hyperparameter tuning, the ANN had the greatest prediction accuracy of 78.9 percent. This study confirms that the data from the Thai government procurement system can be investigated using machine learning techniques from big data technologies. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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15 pages, 3545 KiB  
Article
Prediction of Daily Temperature Based on the Robust Machine Learning Algorithms
by Yu Li, Tongfei Li, Wei Lv, Zhiyao Liang and Junxian Wang
Sustainability 2023, 15(12), 9289; https://doi.org/10.3390/su15129289 - 08 Jun 2023
Viewed by 1459
Abstract
Temperature climate is an essential component of weather forecasting and is vital in predicting future weather patterns. Accurate temperature predictions can assist individuals and organizations in preparing for potential weather-related events such as heat waves or cold snaps. However, achieving precise temperature predictions [...] Read more.
Temperature climate is an essential component of weather forecasting and is vital in predicting future weather patterns. Accurate temperature predictions can assist individuals and organizations in preparing for potential weather-related events such as heat waves or cold snaps. However, achieving precise temperature predictions necessitates thoroughly comprehending the underlying factors influencing climate patterns. The study utilized two models, LSTM and DLSTM, to forecast daily air temperature using 1097 data points gathered from central and southern regions of Tabriz city of Iran in Asia from 2017 to 2019. The results indicated that the proposed model had a high accuracy rate for predicting daily air temperature for test data, with RMSEDLSTM = 0.08 °C and R-SquareDLSTM = 0.99. The DLSTM algorithm is known for its high speed, accuracy, time series prediction, noise reduction capabilities for data, the large volume of data processing, and improved performance of predicted data. In summary, while both LSTM and DLSTM are used for predicting data points, DLSTM is a more advanced version that includes multiple layers of memory cells and is better suited for handling complex sequences of events. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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18 pages, 3328 KiB  
Article
A Machine Learning-Based Decision Support System for Predicting and Repairing Cracks in Undisturbed Loess Using Microbial Mineralization and the Internet of Things
by Yangyang Yue and Yiqing Lv
Sustainability 2023, 15(10), 8269; https://doi.org/10.3390/su15108269 - 18 May 2023
Cited by 2 | Viewed by 1088
Abstract
Recent years have seen a significant increase in interest across several sectors in the application of learning techniques to extract ground object information, such as soil cracks, from remote sensing high-resolution images. Out of the many technologies, the microbial-induced carbonate deposition (MICP) technology [...] Read more.
Recent years have seen a significant increase in interest across several sectors in the application of learning techniques to extract ground object information, such as soil cracks, from remote sensing high-resolution images. Out of the many technologies, the microbial-induced carbonate deposition (MICP) technology is used to inject bacteria and cementation liquid containing specific bacteria into the cracks of soil to be repaired. Calcium carbonate types of cement are produced by bacterial metabolism so that cracks in the soil could be repaired for disaster management. However, detection of cracks and taking appropriate decisions for repairing are the most fundamental issues that researchers’ attention. Machine learning algorithms can be trained to detect and predict cracks in undisturbed loess using various data sources, such as images captured using the internet of things (IoT), devices, drones, and/or ground-based sensors. These algorithms can be designed to identify different types of cracks based on their shapes, sizes, and orientations, and can be trained on large datasets of labelled crack images to improve their accuracy over time. In this paper, we propose a decision support system (DSS) that detects and predicts cracks and recommends a suitable crack repair methodology. Our results show that our system is highly accurate. Our system provides real-time recommendations to engineers working on crack repair projects in undisturbed loess, guiding them on where and how to apply microbial mineralization treatments based on the predicted crack locations and treatment effectiveness. We noted that the accuracy of the crack detection and prediction can be increased significantly (up to 9.57%) when the proposed DSS approach is considered. Moreover, if PSO is implemented as the optimization model, then we can see that the accuracy can be significantly improved by as much as 21.67% to no DSS approach and 11.32% to the DSS approach. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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18 pages, 405 KiB  
Article
An Improved Partitioning Method via Disassociation towards Environmental Sustainability
by Asma Alshuhail and Surbhi Bhatia
Sustainability 2023, 15(9), 7447; https://doi.org/10.3390/su15097447 - 30 Apr 2023
Viewed by 1037
Abstract
The amount of data created by individuals increases daily. These data may be gathered from various sources, such as social networks, e-commerce websites and healthcare systems, and they are frequently made available to third-party research and commercial organisations to facilitate a wide range [...] Read more.
The amount of data created by individuals increases daily. These data may be gathered from various sources, such as social networks, e-commerce websites and healthcare systems, and they are frequently made available to third-party research and commercial organisations to facilitate a wide range of data studies. The protection of sensitive and confidential information included within the datasets to be published must be addressed, even though publishing data can assist organisations in improving their service offerings and developing new solutions that would not otherwise be available. The research community has invested great effort over the past two decades to comprehend how individuals’ privacy may be preserved when their data need to be published. Disassociation is a common approach for anonymising transactional data against re-identification attacks in privacy-preserving data publishing. To address this issue, we proposed three new strategies for horizontal partitioning: suppression, adding and remaining list. Each strategy identifies a different approach for handling small clusters with fewer than k transactions. We used three real datasets for transactional data in our experiments, and our findings showed that our proposed strategies could decrease the percentage of information loss of disassociated transactional data by almost 35%, comparing it with the previous original disassociation algorithm. As a result, the utility of published data will be improved. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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21 pages, 6815 KiB  
Article
Analyzing and Managing Various Energy-Related Environmental Factors for Providing Personalized IoT Services for Smart Buildings in Smart Environment
by Prabhakar Krishnan, A V Prabu, Sumathi Loganathan, Sidheswar Routray, Uttam Ghosh and Mohammed AL-Numay
Sustainability 2023, 15(8), 6548; https://doi.org/10.3390/su15086548 - 12 Apr 2023
Cited by 4 | Viewed by 2062
Abstract
More energy is consumed by domestic appliances all over the world. By reducing energy consumption, sustainability can be improved in domestic contexts. Several earlier approaches to this problem have provided a conceptual overview of green and smart buildings. This paper aims to provide [...] Read more.
More energy is consumed by domestic appliances all over the world. By reducing energy consumption, sustainability can be improved in domestic contexts. Several earlier approaches to this problem have provided a conceptual overview of green and smart buildings. This paper aims to provide a better solution for reducing energy consumption by identifying the fields of abnormal energy consumption. It creates a better environment-friendly smart building to adopt the various lifestyles of people. This paper’s main objective is to monitor and control the energy efficiency of smart buildings by integrating IoT sensors. This paper mainly analyzes various prime factors that can help to improve energy efficiency in smart buildings. Factors impacting energy consumption are analyzed, and outliers of energy consumption are predicted and optimized to save energy. Various parameters are derived from IoT devices to improve energy efficiency in lighting and HVAC controls, energy monitoring, building envelope and automation systems, and renewable energy. The parameters used in water, network convergence, and electrical and environmental monitoring are also used for improving energy efficiency. This paper uses various IoT devices for monitoring and generating data in and around a smart building and analyzes it by implementing an intelligent Information Communication Technology (ICT) model called the Dynamic Semantic Behavior Data Analysis (DSBDA) Model to analyze data concerning dynamic changes in the environment and user behavior to improve energy efficiency and provide better sustainable lifestyle-based smart buildings. From the analyzed output, the outliers of the power consumption and other abnormalities are identified and controlled manually or automatically to improve sustainability regarding energy use in smart buildings. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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18 pages, 13029 KiB  
Article
A Robust Fuzzy Equilibrium Optimization-Based ROI Selection and DWT-Based Multi-Watermarking Model for Medical Images
by Surbhi Bhatia and Alhanof Almutairi
Sustainability 2023, 15(7), 6189; https://doi.org/10.3390/su15076189 - 04 Apr 2023
Cited by 2 | Viewed by 1491
Abstract
Image watermarking is the process of securely embedding a higher amount of information in the host object. These processes ensure authentication, image integration, and content verification. Several existing methods face complicated problems, such as security issues, robustness, and data leakage. Therefore, researchers developed [...] Read more.
Image watermarking is the process of securely embedding a higher amount of information in the host object. These processes ensure authentication, image integration, and content verification. Several existing methods face complicated problems, such as security issues, robustness, and data leakage. Therefore, researchers developed specific methods for different applications. However, the performance of the currently obtained method was lower due to their low resistances. Therefore, to overcome this issue, we employed a novel technique, a fuzzy equilibrium optimization (FEO) approach, for embedding water image encryption. Initially, the raw image undergoes fuzzification to determine the critical point; thus, the intensity of the radial line selects a region of interest (ROI). Finally, the watermarking images are converted into a time-frequency domain via discrete wavelet transform (DWT), where the sub-band is converted based on value of magnitude. The proposed technique is analyzed using three medical image datasets, namely magnetic resonance imaging (MRI), ultrasound (US), and computed tomography (CT) datasets. However, all pixels in each sub-band are replaced to form a fully encrypted image, guaranteeing a watermarked reliable, secure, non-breakable format. Singular values are obtained for the encrypted watermarking image to provide high robustness to the watermarked image. After validation, the proposed fuzzy equilibrium optimization technique achieved higher robustness and security against different types of attacks. Moreover, the proposed FEO technique achieved a value of peak signal to noise ratio (PSNR) about 42.5 dB higher than other compared techniques. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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14 pages, 1366 KiB  
Article
Data-Driven Analysis of Privacy Policies Using LexRank and KL Summarizer for Environmental Sustainability
by Abdul Quadir Md, Raghav V. Anand, Senthilkumar Mohan, Christy Jackson Joshua, Sabhari S. Girish, Anthra Devarajan and Celestine Iwendi
Sustainability 2023, 15(7), 5941; https://doi.org/10.3390/su15075941 - 29 Mar 2023
Viewed by 1404
Abstract
Natural language processing (NLP) is a field in machine learning that analyses and manipulate huge amounts of data and generates human language. There are a variety of applications of NLP such as sentiment analysis, text summarization, spam filtering, language translation, etc. Since privacy [...] Read more.
Natural language processing (NLP) is a field in machine learning that analyses and manipulate huge amounts of data and generates human language. There are a variety of applications of NLP such as sentiment analysis, text summarization, spam filtering, language translation, etc. Since privacy documents are important and legal, they play a vital part in any agreement. These documents are very long, but the important points still have to be read thoroughly. Customers might not have the necessary time or the knowledge to understand all the complexities of a privacy policy document. In this context, this paper proposes an optimal model to summarize the privacy policy in the best possible way. The methodology of text summarization is the process where the summaries from the original huge text are extracted without losing any vital information. Using the proposed idea of a common word reduction process combined with natural language processing algorithms, this paper extracts the sentences in the privacy policy document that hold high weightage and displays them to the customer, and it can save the customer’s time from reading through the entire policy while also providing the customers with only the important lines that they need to know before signing the document. The proposed method uses two different extractive text summarization algorithms, namely LexRank and Kullback Leibler (KL) Summarizer, to summarize the obtained text. According to the results, the summarized sentences obtained via the common word reduction process and text summarization algorithms were more significant than the raw privacy policy text. The introduction of this novel methodology helps to find certain important common words used in a particular sector to a greater depth, thus allowing more in-depth study of a privacy policy. Using the common word reduction process, the sentences were reduced by 14.63%, and by applying extractive NLP algorithms, significant sentences were obtained. The results after applying NLP algorithms showed a 191.52% increase in the repetition of common words in each sentence using the KL summarizer algorithm, while the LexRank algorithm showed a 361.01% increase in the repetition of common words. This implies that common words play a large role in determining a sector’s privacy policies, making our proposed method a real-world solution for environmental sustainability. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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20 pages, 2424 KiB  
Article
An Overview of Blockchain and IoT Integration for Secure and Reliable Health Records Monitoring
by Shadab Alam, Surbhi Bhatia, Mohammed Shuaib, Mousa Mohammed Khubrani, Fayez Alfayez, Areej A. Malibari and Sadaf Ahmad
Sustainability 2023, 15(7), 5660; https://doi.org/10.3390/su15075660 - 23 Mar 2023
Cited by 15 | Viewed by 2535
Abstract
The Internet of Things (IoT) and blockchain (BC) are reliable technologies widely employed in various contexts. IoT devices have a lot of potential for data sensing and recording without human intervention, but they also have processing and security issues. Due to their limited [...] Read more.
The Internet of Things (IoT) and blockchain (BC) are reliable technologies widely employed in various contexts. IoT devices have a lot of potential for data sensing and recording without human intervention, but they also have processing and security issues. Due to their limited computing power, IoT devices cannot use specialized cryptographic security mechanisms. There are various challenges when using traditional cryptographic techniques to transport and store medical records securely. The general public’s health depends on having an electronic health record (EHR) system that is current. In the era of e-health and m-health, problems with integrating data from various EHRs, preserving data interoperability, and ensuring that all data access is in the patient’s hands are all obstacles to creating a dependable EHR system. If health records get into the wrong hands, they could endanger the lives of patients and their right to privacy. BC technology has become a potent tool for ensuring recorded data’s immutability, validity, and confidentiality while enabling decentralized storage. This study focuses on EHR and other types of e-healthcare, evaluating the advantages of complementary technologies and the underlying functional principles. The major BC consensus mechanisms for BC-based EHR systems are analyzed in this study. It also examines several IoT-EHR frameworks’ current infrastructures. A breakdown of BC integration’s benefits with the IoT-EHR framework is also offered. A BC-based IoT-EHR architecture has been developed to enable the automated sensing of patient records and to store and retrieve these records in a secure and reliable environment. Finally, we conduct a security study to demonstrate the security of our suggested EHR framework. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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16 pages, 2402 KiB  
Article
Sentiment Computation of UK-Originated COVID-19 Vaccine Tweets: A Chronological Analysis and News Effect
by Olasoji Amujo, Ebuka Ibeke, Richard Fuzi, Ugochukwu Ogara and Celestine Iwendi
Sustainability 2023, 15(4), 3212; https://doi.org/10.3390/su15043212 - 09 Feb 2023
Cited by 4 | Viewed by 1536
Abstract
This study aimed to analyse public sentiments of UK-originated tweets related to COVID-19 vaccines, and it applied six chronological time periods, between January and December 2021. The dates were related to six BBC news reports about the most significant developments in the three [...] Read more.
This study aimed to analyse public sentiments of UK-originated tweets related to COVID-19 vaccines, and it applied six chronological time periods, between January and December 2021. The dates were related to six BBC news reports about the most significant developments in the three main vaccines that were being administered in the UK at the time: Pfizer-BioNTech, Moderna, and Oxford-AstraZeneca. Each time period spanned seven days, starting from the day of the news report. The study employed the bidirectional encoder representations from transformers (BERT) model to analyse the sentiments in 4172 extracted tweets. The BERT model adopts the transformer architecture and uses masked language and next sentence prediction models. The results showed that the overall sentiments for all three vaccines were negative across all six periods, with Moderna having the least negative tweets and the highest percentage of positive tweets overall while AstraZeneca attracted the most negative tweets. However, for all the considered time periods, Period 3 (23–29 May 2021) received the least negative and the most positive tweets, following the related BBC report—’COVID: Pfizer and AstraZeneca jabs work against Indian variant’—despite reports of blood clots associated with AstraZeneca during the same time period. Time periods 5 and 6 had no breaking news related to COVID vaccines, and they reflected no significant changes. We, therefore, concluded that the BBC news reports on COVID vaccines significantly impacted public sentiments regarding the COVID-19 vaccines. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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16 pages, 1799 KiB  
Article
A Blockchain-Driven Food Supply Chain Management Using QR Code and XAI-Faster RCNN Architecture
by Surbhi Bhatia and Abdulaziz Saad Albarrak
Sustainability 2023, 15(3), 2579; https://doi.org/10.3390/su15032579 - 31 Jan 2023
Cited by 7 | Viewed by 3355
Abstract
The availability of food in a country and the capacity of its citizens to access, acquire, and receive enough food are both referred to as having food security. A crucial component of food security is ensuring and maintaining safe and high-quality goods, which [...] Read more.
The availability of food in a country and the capacity of its citizens to access, acquire, and receive enough food are both referred to as having food security. A crucial component of food security is ensuring and maintaining safe and high-quality goods, which the supply chain process should take into due deliberation. To enhance the food supply chain, organic and wholesome food items should be encouraged. Although packaged goods are evaluated and approved by legal authorities, there is no mechanism in place for testing and assessing the market’s available supply on a regular basis. As a result, food manufacturers are compelled to provide nutritious and healthy products. In this research, we propose an explainable artificial intelligence-based faster regions with convolutional neural networks (XAI-based Faster RCNN) model to evaluate the contents of the food items through user-friendly web-based front-end design and QR code. To validate each communication token in the network, an elliptic curve integrated encrypted scheme (ECIES) based on blockchain technology is utilized. Additionally, artificial rabbit optimization (ARO) is used to register each user and assign him a key. The user will gain a deeper understanding of machine learning (ML) and AI applications using the XAI technique. An EAI-based Faster RCNN model is proposed to help digitize information about food products, rapidly retrieve the information, and discover any hidden information in the quick response (QR) code that could have impacted the safety and quality of the food. The results of the experiments indicated that the proposed method requires less response time than other existing methods with the increase of payload and users. The Shapley additive explanation is used to obtain a legal plea for the laboratory test based on the nutritional information present in the QR code. The benefits provided by ECIES-based blockchain technology assist policymakers, manufacturers, and merchants in efficient decision-making, minimizing public health hazards, and improving welfare. This paper also shows that the accuracy achieved by the proposed method reached 99.53%, with the lowest processing time. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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18 pages, 4589 KiB  
Article
FSPV-Grid System for an Industrial Subsection with PV Price Sensitivity Analysis
by Tanu Rizvi, Satya Prakash Dubey, Nagendra Tripathi, Gautam Srivastava, Satya Prakash Makhija and Md. Khaja Mohiddin
Sustainability 2023, 15(3), 2495; https://doi.org/10.3390/su15032495 - 30 Jan 2023
Cited by 7 | Viewed by 1090
Abstract
Renewable energy sources, particularly solar photovoltaic generation, now dominate generation options. Solar generation advancements have resulted in floating solar photovoltaics, also known as FSPV systems. FSPV systems are one of the fastest growing technologies today, providing a viable replacement for ground-mounted PV systems [...] Read more.
Renewable energy sources, particularly solar photovoltaic generation, now dominate generation options. Solar generation advancements have resulted in floating solar photovoltaics, also known as FSPV systems. FSPV systems are one of the fastest growing technologies today, providing a viable replacement for ground-mounted PV systems due to their flexibility and low land-space requirement. This paper presents a systematic approach for implementing a proposed FSPV–grid integrated system in Bhilai Steel Plant’s (BSP) subsections. BSP is a steel manufacturing plant located in Bhilai, Chhattisgarh, and the FSPV system has the potential to generate sufficient energy by accessing two of its reservoirs. The system was simulated in HOMER Pro software, which provided the FSPV system power estimations, area requirements, net present cost (NPC), levelized cost of energy (LCOE), production summary, grid purchasing/selling, IRR, ROI, paybacks and pollutant emissions. A sensitivity analysis for a hike in PV prices globally due to a shortage in poly silicone in international markets during the fiscal year 2021–2022 was undertaken for the proposed FSPV–grid system. Here, the authors considered hikes in the PV price of 1%, 9%and 18% respectively, since the maximum percentage increase in PV prices globally is 18%. The authors also compared the proposed FSPV–grid system to the existing grid-only system for two sections of the BSP and the results obtained showed that the NPC and LCOE would be much lower in the case of the FSPV–grid system than the grid-only system. However, with changes in the percentage hike in PV prices, the NPC and LCOE were found to increase due to changes in the proportion of FSPV–grid systems in production. The pollutant emissions were the minimum in the case of the FSPV–grid system, whereas they were the highest in the case of the existing grid-only system. Furthermore, the payback analysis indicated that the minimum ROI for the above-defined construction would be fully covered in 15.81 years with the nominal 1% pricing for FSPV–grid generation. Therefore, the overall results suggest that the FSPV–grid system has the potential to be a perfect alternative solar energy source that can meet the current electrical energy requirements of the steel manufacturing industry with nominal pricing better than the existing grid-only system, as well as addressing economic constraints and conferring environmental benefits. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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12 pages, 669 KiB  
Article
A Novel Hybrid Severity Prediction Model for Blast Paddy Disease Using Machine Learning
by Shweta Lamba, Vinay Kukreja, Anupam Baliyan, Shalli Rani and Syed Hassan Ahmed
Sustainability 2023, 15(2), 1502; https://doi.org/10.3390/su15021502 - 12 Jan 2023
Cited by 39 | Viewed by 1883
Abstract
Hypothesis: Due to the increase in the losses in paddy yield as a result of various paddy diseases, researchers are working tirelessly for a technological solution to assist farmers in making decisions about disease severity and potential danger to the crop. Early [...] Read more.
Hypothesis: Due to the increase in the losses in paddy yield as a result of various paddy diseases, researchers are working tirelessly for a technological solution to assist farmers in making decisions about disease severity and potential danger to the crop. Early prediction of infection severity would facilitate resources for the treatment of the infection and prevent contamination to the whole field. Methodology: In this study, a hybrid prediction model was developed to predict various levels of severity of blast disease based on diseased plant images. The proposed model is a four-fold severity prediction model. The level of severity is defined based on the percentage of leaf area affected by the disease. The image dataset is derived from both primary and secondary resources. Tools: The features are first extracted with the help of the Convolutional Neural Network (CNN) approach. Then the identification and classification of the severity level of blast disease are conducted using a Support Vector Machine (SVM). Conclusion: Mendeley, Kaggle, GitHub, and UCI are the secondary resources used for dataset generation. The number of images in the dataset is 1908. The proposed hybrid model achieves 97% accuracy. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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16 pages, 3946 KiB  
Article
Comprehensive Database Creation for Potential Fish Zones Using IoT and ML with Assimilation of Geospatial Techniques
by Sanjeev Kimothi, Asha Thapliyal, Rajesh Singh, Mamoon Rashid, Anita Gehlot, Shaik Vaseem Akram and Abdul Rehman Javed
Sustainability 2023, 15(2), 1062; https://doi.org/10.3390/su15021062 - 06 Jan 2023
Cited by 4 | Viewed by 1624
Abstract
The framework for aqua farming database collection and the real-time monitoring of different working functions of aqua farming are essential to enhance and digitalize aqua farming. Data collection and real-time monitoring are attained using cutting-edge technologies, and these cutting-edge technologies are useful for [...] Read more.
The framework for aqua farming database collection and the real-time monitoring of different working functions of aqua farming are essential to enhance and digitalize aqua farming. Data collection and real-time monitoring are attained using cutting-edge technologies, and these cutting-edge technologies are useful for the conservation and advancement of traditional aquatic farming, particularly in hilly areas with sustainable development goals (SDGs). Geo-tagging and geo-mapping of the aqua resources will play an important role in monitoring the species in the aquatic environment and can track the real-time health status, movement, and location, and monitor the foraging behaviors, of aquatic species. This study proposed an architecture with the IoT to manage the aqua resource for eco-sustainability with geospatial data. This study also discussed the geo information systems (GIS)- and geo positioning system (GPS)-based web-based framework for the fisheries sector and the creation of a database for aqua resource management. In the study, the results of database generation for the aqua resource management and the results of the fishpond in the cloud server are presented in detail. Machine learning (ML) is integrated with the framework to analyze the sensor data and geo-spatial data for the identification of any degradation in the water quality. This will provide real-time information to the policymakers for their critical decisions for the further development of aquatic species for enhancing the economy of the state as well as aqua farmers. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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19 pages, 1337 KiB  
Article
Particle Swarm-Based Federated Learning Approach for Early Detection of Forest Fires
by Y. Supriya and Thippa Reddy Gadekallu
Sustainability 2023, 15(2), 964; https://doi.org/10.3390/su15020964 - 05 Jan 2023
Cited by 10 | Viewed by 2163
Abstract
Forests are a vital part of the ecological system. Forest fires are a serious issue that may cause significant loss of life and infrastructure. Forest fires may occur due to human or man-made climate effects. Numerous artificial intelligence-based strategies such as machine learning [...] Read more.
Forests are a vital part of the ecological system. Forest fires are a serious issue that may cause significant loss of life and infrastructure. Forest fires may occur due to human or man-made climate effects. Numerous artificial intelligence-based strategies such as machine learning (ML) and deep learning (DL) have helped researchers to predict forest fires. However, ML and DL strategies pose some challenges such as large multidimensional data, communication lags, transmission latency, lack of processing power, and privacy concerns. Federated Learning (FL) is a recent development in ML that enables the collection and process of multidimensional, large volumes of data efficiently, which has the potential to solve the aforementioned challenges. FL can also help in identifying the trends based on the geographical locations that can help the authorities to respond faster to forest fires. However, FL algorithms send and receive large amounts of weights of the client-side trained models, and also it induces significant communication overhead. To overcome this issue, in this paper, we propose a unified framework based on FL with a particle swarm-optimization algorithm (PSO) that enables the authorities to respond faster to forest fires. The proposed PSO-enabled FL framework is evaluated by using multidimensional forest fire image data from Kaggle. In comparison to the state-of-the-art federated average model, the proposed model performed better in situations of data imbalance, incurred lower communication costs, and thus proved to be more network efficient. The results of the proposed framework have been validated and 94.47% prediction accuracy has been recorded. These results obtained by the proposed framework can serve as a useful component in the development of early warning systems for forest fires. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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23 pages, 6178 KiB  
Article
Feature-Weighting-Based Prediction of Drought Occurrence via Two-Stage Particle Swarm Optimization
by Karpagam Sundararajan and Kathiravan Srinivasan
Sustainability 2023, 15(2), 929; https://doi.org/10.3390/su15020929 - 04 Jan 2023
Cited by 3 | Viewed by 1155
Abstract
Drought directly affects environmental sustainability. Predicting the drought at the earliest opportunity will help to execute drought mitigation plans. Several drought indices are used to predict the severity of drought across different geographical regions. The two main drought indices used in India for [...] Read more.
Drought directly affects environmental sustainability. Predicting the drought at the earliest opportunity will help to execute drought mitigation plans. Several drought indices are used to predict the severity of drought across different geographical regions. The two main drought indices used in India for meteorological drought are the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI). This work is a study to find the ability of above mentioned indices to predict meteorological drought for the state of Tamil Nadu using 62 years of data. The prediction results are evaluated using the performance metrics of precision, recall, f1 score, Matthews correlation coefficient, and accuracy. The dataset is severely imbalanced due to the low number of drought incidence years. Hence there exists a tug of war between precision and recall, so for improving it without affecting one another, a multi-objective optimization process is applied. The prediction performance is improved by using the filter-global-supervised feature weighting and wrapper-global-supervised feature weighting techniques. In the filter-based feature weighting approach, the information gain measure and Pearson correlation coefficient are used as feature weights. For the wrapper-based feature weighting approach, two-stage particle swarm optimization (PSO) is designed to calculate the weights of the features, and the random forest is used as the classifier. This two-stage PSO constructs the best population set for individual objectives and then searches around it to find the best particle so that the multiple contradicting objectives will converge into the best solution easier. When compared to classification without feature weighting, two-stage PSO feature weighting achieves a 45% improvement in precision. However, only a moderate improvement in recall is obtained. According to the findings, SPI3 and SPEI12 should be given more weightage in metrological drought prediction. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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15 pages, 1040 KiB  
Article
Classification of Electrocardiogram Signals Based on Hybrid Deep Learning Models
by Surbhi Bhatia, Saroj Kumar Pandey, Ankit Kumar and Asma Alshuhail
Sustainability 2022, 14(24), 16572; https://doi.org/10.3390/su142416572 - 10 Dec 2022
Cited by 6 | Viewed by 1755
Abstract
According to the analysis of the World Health Organization (WHO), the diagnosis and treatment of heart diseases is the most difficult task. Several algorithms for the classification of arrhythmic heartbeats from electrocardiogram (ECG) signals have been developed over the past few decades, using [...] Read more.
According to the analysis of the World Health Organization (WHO), the diagnosis and treatment of heart diseases is the most difficult task. Several algorithms for the classification of arrhythmic heartbeats from electrocardiogram (ECG) signals have been developed over the past few decades, using computer-aided diagnosis systems. Deep learning architecture adaption is a recent effective advancement of deep learning techniques in the field of artificial intelligence. In this study, we developed a new deep convolutional neural network (CNN) and bidirectional long-term short-term memory network (BLSTM) model to automatically classify ECG heartbeats into five different groups based on the ANSI-AAMI standard. End-to-end learning (feature extraction and classification work together) is done in this hybrid model without extracting manual features. The experiment is performed on the publicly accessible PhysioNet MIT-BIH arrhythmia database, and the findings are compared with results from the other two hybrid deep learning models, which are a combination of CNN and LSTM and CNN and Gated Recurrent Unit (GRU). The performance of the model is also compared with existing works cited in the literature. Using the SMOTE approach, this database was artificially oversampled to address the class imbalance problem. This new hybrid model was trained on the oversampled ECG database and validated using tenfold cross-validation on the actual test dataset. According to experimental observations, the developed hybrid model outperforms in terms of recall, precision, accuracy and F-score performance of the hybrid model are 94.36%, 89.4%, 98.36% and 91.67%, respectively, which is better than the existing methods. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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22 pages, 4688 KiB  
Article
Design and Validation of Lifetime Extension Low Latency MAC Protocol (LELLMAC) for Wireless Sensor Networks Using a Hybrid Algorithm
by Tao Hai, Jincheng Zhou, T. V. Padmavathy, Abdul Quadir Md, Dayang N. A. Jawawi and Muammer Aksoy
Sustainability 2022, 14(23), 15547; https://doi.org/10.3390/su142315547 - 22 Nov 2022
Cited by 8 | Viewed by 1202
Abstract
As the battery-operated power source of wireless sensor networks, energy consumption is a major concern. The medium-access protocol design solves the energy usage of sensor nodes while transmitting and receiving data, thereby improving the sensor network’s lifetime. The suggested work employs a hybrid [...] Read more.
As the battery-operated power source of wireless sensor networks, energy consumption is a major concern. The medium-access protocol design solves the energy usage of sensor nodes while transmitting and receiving data, thereby improving the sensor network’s lifetime. The suggested work employs a hybrid algorithm to improve the energy efficiency of sensor networks with nodes that are regularly placed. Every node in this protocol has three operating modes, which are sleep mode, receive mode, and send mode. Every node enters a periodic sleep state in order to conserve energy, and after waking up, it waits for communication. During the sleep mode, the nodes turn off their radios in order to reduce the amount of energy they consume while not in use. As an added feature, this article offers a channel access mechanism in which the sensors can send data based on the Logical Link Decision (LLD) algorithm and receive data based on the adaptive reception method. It is meant to select acceptable intermediary nodes in order to identify the path from the source to the destination and to minimize data transmission delays among the nodes in the network scenario. Aside from that, both simulation and analytical findings are used to examine the activity of the suggested MAC, and the created models are evaluated depending on their performance. With regard to energy consumption, latency, throughput, and power efficiency, the result demonstrates that the suggested MAC protocol outperforms the corresponding set of rules. The extensive simulation and analytical analysis showed that the energy consumption of the proposed LELLMAC protocol is reduced by 22% and 76.9% the end-to-end latency is 84.7% and 87.4% percent shorter, and the throughput is 60.3% and 70.5% higher than the existing techniques when the number of node is 10 and 100 respectively. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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19 pages, 4217 KiB  
Article
Time Series Data Modeling Using Advanced Machine Learning and AutoML
by Ahmad Alsharef, Sonia, Karan Kumar and Celestine Iwendi
Sustainability 2022, 14(22), 15292; https://doi.org/10.3390/su142215292 - 17 Nov 2022
Cited by 15 | Viewed by 3606
Abstract
A prominent area of data analytics is “timeseries modeling” where it is possible to forecast future values for the same variable using previous data. Numerous usage examples, including the economy, the weather, stock prices, and the development of a corporation, demonstrate its significance. [...] Read more.
A prominent area of data analytics is “timeseries modeling” where it is possible to forecast future values for the same variable using previous data. Numerous usage examples, including the economy, the weather, stock prices, and the development of a corporation, demonstrate its significance. Experiments with time series forecasting utilizing machine learning (ML), deep learning (DL), and AutoML are conducted in this paper. Its primary contribution consists of addressing the forecasting problem by experimenting with additional ML and DL models and AutoML frameworks and expanding the AutoML experimental knowledge. In addition, it contributes by breaking down barriers found in past experimental studies in this field by using more sophisticated methods. The datasets this empirical research utilized were secondary quantitative data of the real prices of the currently most used cryptocurrencies. We found that AutoML for timeseries is still in the development stage and necessitates more study to be a viable solution since it was unable to outperform manually designed ML and DL models. The demonstrated approaches may be utilized as a baseline for predicting timeseries data. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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16 pages, 2205 KiB  
Article
An Ensemble Machine Learning Technique for Detection of Abnormalities in Knee Movement Sustainability
by Hunish Bansal, Basavraj Chinagundi, Prashant Singh Rana and Neeraj Kumar
Sustainability 2022, 14(20), 13464; https://doi.org/10.3390/su142013464 - 19 Oct 2022
Cited by 7 | Viewed by 1639
Abstract
The purpose of this study was to determine electromyographically if there are significant differences in the movement associated with the knee muscle, gait, leg extension from a sitting position and flexion of the leg upwards for regular and abnormal sEMG data. Surface electromyography [...] Read more.
The purpose of this study was to determine electromyographically if there are significant differences in the movement associated with the knee muscle, gait, leg extension from a sitting position and flexion of the leg upwards for regular and abnormal sEMG data. Surface electromyography (sEMG) data were obtained from the lower limbs of 22 people during three different exercises: sitting, standing, and walking (11 with and 11 without knee abnormality). Participants with a knee deformity took longer to finish the task than the healthy subjects. The sEMG signal duration of patients with abnormalities was longer than that of healthy patients, resulting in an imbalance in the obtained sEMG signal data. As a result of the data’s bias towards the majority class, developing a classification model for automated analysis of such sEMG signals is arduous. The sEMG collected data were denoised and filtered, followed by the extraction of time-domain characteristics. Machine learning methods were then used for predicting the three distinct movements (sitting, standing, and walking) associated with electrical impulses for normal and abnormal sets. Different anomaly detection techniques were also used for detecting occurrences in the sEMG signals that differed considerably from the majority of data and were hence used for enhancing the performance of our model. The iforest anomaly detection technique presented in this work can achieve 98.5% accuracy on the light gradient boosting machine algorithm, surpassing the previous results which claimed a maximum accuracy of 92.5% and 91%, improving accuracy by 6–7% for classification of knee abnormality using machine learning. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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17 pages, 2641 KiB  
Article
A Study of the Impacts of Air Pollution on the Agricultural Community and Yield Crops (Indian Context)
by Sharnil Pandya, Thippa Reddy Gadekallu, Praveen Kumar Reddy Maddikunta and Rohit Sharma
Sustainability 2022, 14(20), 13098; https://doi.org/10.3390/su142013098 - 13 Oct 2022
Cited by 19 | Viewed by 2668
Abstract
Air pollution has been an vital issue throughout the 21st century, and has also significantly impacted the agricultural community, especially farmers and yield crops. This work aims to review air-pollution research to understand its impacts on the agricultural community and yield crops, specifically [...] Read more.
Air pollution has been an vital issue throughout the 21st century, and has also significantly impacted the agricultural community, especially farmers and yield crops. This work aims to review air-pollution research to understand its impacts on the agricultural community and yield crops, specifically in developing countries, such as India. The present work highlights various aspects of agricultural damage caused by the impacts of air pollution. Furthermore, in the undertaken study, a rigorous and detailed discussion of state-wise and city-wise yield-crop losses caused by air pollution in India and its impacts has been performed. To represent air-pollution impacts, the color-coding-based AQI (Air Quality Index) risk-classification metrics have been used to represent AQI variations in India’s agrarian states and cities. Finally, recent impacts of air pollution concerning AQI variations for May 2019 to February 2020, Seasonal AQI variations, impacts of PM2.5, and PM10 in various agrarian states and India cities are presented using various tabular and graphical representations. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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24 pages, 8267 KiB  
Article
Curve-Aware Model Predictive Control (C-MPC) Trajectory Tracking for Automated Guided Vehicle (AGV) over On-Road, In-Door, and Agricultural-Land
by Sundaram Manikandan, Ganesan Kaliyaperumal, Saqib Hakak and Thippa Reddy Gadekallu
Sustainability 2022, 14(19), 12021; https://doi.org/10.3390/su141912021 - 23 Sep 2022
Cited by 15 | Viewed by 2398
Abstract
Navigating the AGV over the curve path is a difficult problem in all types of navigation (landmark, behavior, vision, and GPS). A single path tracking algorithm is required to navigate the AGV in a mixed environment that includes indoor, on-road, and agricultural terrain. [...] Read more.
Navigating the AGV over the curve path is a difficult problem in all types of navigation (landmark, behavior, vision, and GPS). A single path tracking algorithm is required to navigate the AGV in a mixed environment that includes indoor, on-road, and agricultural terrain. In this paper, two types of proposed methods are presented. First, the curvature information from the generated trajectory (path) data is extracted. Second, the improved curve-aware MPC (C-MPC) algorithm navigates AGV in a mixed environment. The results of the real-time experiments demonstrated that the proposed curve finding algorithm successfully extracted curves from all types of terrain (indoor, on-road, and agricultural-land) path data with low type 1 (percentage of the unidentified curve) and type 2 (extra waypoints added to identified curve) errors, and eliminated path noise (hand-drawn line error over map). The AGV was navigated using C-MPC, and the real-time and simulation results reveal that the proposed path tracking technique for the mixed environment (indoor, on-road, agricultural-land, and agricultural-land with slippery error) successfully navigated the AGV and had a lower RMSE lateral and longitudinal error than the existing path tracking algorithm. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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18 pages, 1372 KiB  
Article
Predicting Flood Hazards in the Vietnam Central Region: An Artificial Neural Network Approach
by Minh Pham Quang and Krti Tallam
Sustainability 2022, 14(19), 11861; https://doi.org/10.3390/su141911861 - 21 Sep 2022
Cited by 6 | Viewed by 2505
Abstract
Flooding as a hazard has negatively impacted Vietnam’s agriculture, economy, and infrastructure with increasing intensity because of climate change. Flood hazards in Vietnam are difficult to combat, as Vietnam is densely populated with rivers and canals. While there are attempts to lessen the [...] Read more.
Flooding as a hazard has negatively impacted Vietnam’s agriculture, economy, and infrastructure with increasing intensity because of climate change. Flood hazards in Vietnam are difficult to combat, as Vietnam is densely populated with rivers and canals. While there are attempts to lessen the damage through hazard mitigation policies, such as early evacuation warnings, these attempts are made heavily reliant on short-term traditional statistical models and physical hydrology modeling, which provide suboptimal results. The current situation is caused by the fragmented approach from the Vietnamese government and exacerbates a need for more centralized and robust flood predictive systems. Local governments need to employ their own prediction models which often lack the capacity to draw key insights from limited flood occurrences. Given the robustness of machine learning, especially in low data settings, in this study, we attempt to introduce an artificial neural network model with the aim to create long-term forecast and compare it with other machine learning approaches. We trained the models using different variables evaluated under three characteristics: climatic, hydrological, and socio-economic. We found that our artificial neural network model performed substantially better both in performance metrics (91% accuracy) and relative to other models and can predict well flood hazards in the long term. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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19 pages, 6738 KiB  
Article
Cloud-Based Fault Prediction for Real-Time Monitoring of Sensor Data in Hospital Environment Using Machine Learning
by Mudita Uppal, Deepali Gupta, Sapna Juneja, Adel Sulaiman, Khairan Rajab, Adel Rajab, M. A. Elmagzoub and Asadullah Shaikh
Sustainability 2022, 14(18), 11667; https://doi.org/10.3390/su141811667 - 16 Sep 2022
Cited by 21 | Viewed by 2660
Abstract
The amount of data captured is expanding day by day which leads to the need for a monitoring system that helps in decision making. Current technologies such as cloud, machine learning (ML) and Internet of Things (IoT) provide a better solution for monitoring [...] Read more.
The amount of data captured is expanding day by day which leads to the need for a monitoring system that helps in decision making. Current technologies such as cloud, machine learning (ML) and Internet of Things (IoT) provide a better solution for monitoring automation systems efficiently. In this paper, a prediction model that monitors real-time data of sensor nodes in a clinical environment using a machine learning algorithm is proposed. An IoT-based smart hospital environment has been developed that controls and monitors appliances over the Internet using different sensors such as current sensors, a temperature and humidity sensor, air quality sensor, ultrasonic sensor and flame sensor. The IoT-generated sensor data have three important characteristics, namely, real-time, structured and enormous amount. The main purpose of this research is to predict early faults in an IoT environment in order to ensure the integrity, accuracy, reliability and fidelity of IoT-enabled devices. The proposed fault prediction model was evaluated via decision tree, K-nearest neighbor, Gaussian naive Bayes and random forest techniques, but random forest showed the best accuracy over others on the provided dataset. The results proved that the ML techniques applied over IoT-based sensors are well efficient to monitor this hospital automation process, and random forest was considered the best with the highest accuracy of 94.25%. The proposed model could be helpful for the user to make a decision regarding the recommended solution and control unanticipated losses generated due to faults during the automation process. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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19 pages, 587 KiB  
Article
Understanding the Factors Influencing Consumers’ Intention toward Shifting to Solar Energy Technology for Residential Use in Saudi Arabia Using the Technology Acceptance Model
by Waad Bouaguel and Tagreed Alsulimani
Sustainability 2022, 14(18), 11356; https://doi.org/10.3390/su141811356 - 10 Sep 2022
Cited by 4 | Viewed by 2750
Abstract
Over the last few years, the Kingdom of Saudi Arabia has taken significant steps in adopting clean and sustainable energy coming from renewable energy sources. The adoption of solar energy in residential use was one of the main projects in the 2030 Saudi [...] Read more.
Over the last few years, the Kingdom of Saudi Arabia has taken significant steps in adopting clean and sustainable energy coming from renewable energy sources. The adoption of solar energy in residential use was one of the main projects in the 2030 Saudi vision of preserving nature reserves, with sustainability as a key pillar. The Saudi government has granted individuals the right to install solar photovoltaic systems in their homes and has taken many steps to encourage this initiative. However, despite all these efforts to bring solar energy into homes, few applications have been received. Therefore, it is important to examine the various factors that influence Saudi society’s perceptions and attitudes toward the acceptance or rejection of new solar technologies. The Technology Acceptance Model is one of the best technology acceptance frameworks. The model examines intentions and attitudes to adopt new technologies based on two constructs: perceived usefulness and perceived ease of use. In this study, we extend the Technology Acceptance Model by adding new constructs: relative advantages, environmental awareness, and cost of solar photovoltaic systems. These factors were examined by analyzing the intentions of 492 male and female respondents. Data were collected through online surveys. The findings of the study indicated that all the Technology Acceptance Model constructs significantly impact the attitude toward the adoption of solar energy in residential use. These results recommend that the Saudi government should focus on increasing Saudi environment awareness, reconsidering solar PV costs, and putting more emphasis on the relative advantages of solar PV in residential use. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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16 pages, 3160 KiB  
Article
A Deep Learning-Based Model for Date Fruit Classification
by Khalied Albarrak, Yonis Gulzar, Yasir Hamid, Abid Mehmood and Arjumand Bano Soomro
Sustainability 2022, 14(10), 6339; https://doi.org/10.3390/su14106339 - 23 May 2022
Cited by 58 | Viewed by 4996
Abstract
A total of 8.46 million tons of date fruit are produced annually around the world. The date fruit is considered a high-valued confectionery and fruit crop. The hot arid zones of Southwest Asia, North Africa, and the Middle East are the major producers [...] Read more.
A total of 8.46 million tons of date fruit are produced annually around the world. The date fruit is considered a high-valued confectionery and fruit crop. The hot arid zones of Southwest Asia, North Africa, and the Middle East are the major producers of date fruit. The production of dates in 1961 was 1.8 million tons, which increased to 2.8 million tons in 1985. In 2001, the production of dates was recorded at 5.4 million tons, whereas recently it has reached 8.46 million tons. A common problem found in the industry is the absence of an autonomous system for the classification of date fruit, resulting in reliance on only the manual expertise, often involving hard work, expense, and bias. Recently, Machine Learning (ML) techniques have been employed in such areas of agriculture and fruit farming and have brought great convenience to human life. An automated system based on ML can carry out the fruit classification and sorting tasks that were previously handled by human experts. In various fields, CNNs (convolutional neural networks) have achieved impressive results in image classification. Considering the success of CNNs and transfer learning in other image classification problems, this research also employs a similar approach and proposes an efficient date classification model. In this research, a dataset of eight different classes of date fruit has been created to train the proposed model. Different preprocessing techniques have been applied in the proposed model, such as image augmentation, decayed learning rate, model checkpointing, and hybrid weight adjustment to increase the accuracy rate. The results show that the proposed model based on MobileNetV2 architecture has achieved 99% accuracy. The proposed model has also been compared with other existing models such as AlexNet, VGG16, InceptionV3, ResNet, and MobileNetV2. The results prove that the proposed model performs better than all other models in terms of accuracy. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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22 pages, 8259 KiB  
Article
Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization
by Salil Bharany, Sandeep Sharma, Surbhi Bhatia, Mohammad Khalid Imam Rahmani, Mohammed Shuaib and Saima Anwar Lashari
Sustainability 2022, 14(10), 6159; https://doi.org/10.3390/su14106159 - 19 May 2022
Cited by 51 | Viewed by 2582
Abstract
FANET (flying ad-hoc networks) is currently a trending research topic. Unmanned aerial vehicles (UAVs) have two significant challenges: short flight times and inefficient routing due to low battery power and high mobility. Due to these topological restrictions, FANETS routing is considered more complicated [...] Read more.
FANET (flying ad-hoc networks) is currently a trending research topic. Unmanned aerial vehicles (UAVs) have two significant challenges: short flight times and inefficient routing due to low battery power and high mobility. Due to these topological restrictions, FANETS routing is considered more complicated than MANETs or VANETs. Clustering approaches based on artificial intelligence (AI) approaches can be used to solve complex routing issues when static and dynamic routings fail. Evolutionary algorithm-based clustering techniques, such as moth flame optimization, and ant colony optimization, can be used to solve these kinds of problems with routes. Moth flame optimization gives excellent coverage while consuming little energy and requiring a minimum number of cluster heads (CHs) for routing. This paper employs a moth flame optimization algorithm for network building and node deployment. Then, we employ a variation of the K-Means Density clustering approach to choosing the cluster head. Choosing the right cluster heads increases the cluster’s lifespan and reduces routing traffic. Moreover, it lowers the number of routing overheads. This step is followed by MRCQ image-based compression techniques to reduce the amount of data that must be transmitted. Finally, the reference point group mobility model is used to send data by the most optimal path. Particle swarm optimization (PSO), ant colony optimization (ACO), and grey wolf optimization (GWO) were put to the test against our proposed EECP-MFO. Several metrics are used to gauge the efficiency of our proposed method, including the number of clusters, cluster construction time, cluster lifespan, consistency of cluster heads, and energy consumption. This paper demonstrates that our proposed algorithm performance is superior to the current state-of-the-art approaches using experimental results. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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15 pages, 2271 KiB  
Article
Sustainable Network by Enhancing Attribute-Based Selection Mechanism Using Lagrange Interpolation
by Chetna Monga, Deepali Gupta, Devendra Prasad, Sapna Juneja, Ghulam Muhammad and Zulfiqar Ali
Sustainability 2022, 14(10), 6082; https://doi.org/10.3390/su14106082 - 17 May 2022
Cited by 9 | Viewed by 1467
Abstract
The security framework in Ad-hoc Networks (ANET) continues to attract the attention of researchers, although significant work has been accomplished already. Researchers in the last couple of years have shown quite an improvement in Identity Dependent Cryptography (IDC). Security in ANET is hard [...] Read more.
The security framework in Ad-hoc Networks (ANET) continues to attract the attention of researchers, although significant work has been accomplished already. Researchers in the last couple of years have shown quite an improvement in Identity Dependent Cryptography (IDC). Security in ANET is hard to attain due to the vulnerability of links (Wireless). IDC encompasses Polynomial Interpolations (PI) such as Lagrange, curve-fitting, and spline to provide security by implementing Integrated Key Management (IKM). The PI structure trusts all the available nodes in the network and randomly picks nodes for the security key generation. This paper presents a solution to the trust issues raised in Lagrange’s-PI (LI) utilizing an artificial neural network and attribute-based tree structure. The proposed structure not only improves the trust factor but also enhances the accuracy measures of LI to provide a sustainable network system. Throughput, PDR, noise, and latency have been increased by 47%, 50%, 34%, and 30%, respectively, by using LI and incorporating the aforementioned techniques. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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21 pages, 2392 KiB  
Article
System-Level Performance Analysis of Cooperative Multiple Unmanned Aerial Vehicles for Wildfire Surveillance Using Agent-Based Modeling
by Ayesha Maqbool, Alina Mirza, Farkhanda Afzal, Tajammul Shah, Wazir Zada Khan, Yousaf Bin Zikria and Sung Won Kim
Sustainability 2022, 14(10), 5927; https://doi.org/10.3390/su14105927 - 13 May 2022
Cited by 3 | Viewed by 1675
Abstract
In this paper, we propose an agent-based approach for the evaluation of Multiple Unmanned Autonomous Vehicle (MUAV) wildfire monitoring systems for remote and hard-to-reach areas. Emerging environmental factors are causing a higher number of wildfires and keeping these fires in check is becoming [...] Read more.
In this paper, we propose an agent-based approach for the evaluation of Multiple Unmanned Autonomous Vehicle (MUAV) wildfire monitoring systems for remote and hard-to-reach areas. Emerging environmental factors are causing a higher number of wildfires and keeping these fires in check is becoming a global challenge. MUAV deployment for the monitoring and surveillance of potential fires has already been established. However, most of the scholarly work is still focused on MUAV operations details. In wildfire surveillance and monitoring, evaluations of the system-level performance in terms of the analysis of the effects of individual behavior on system surveillance has yet to be established. Especially in an MUAV system, the individual and cooperative behaviors of the team affect the overall performance of the system. Such systems are dynamic and stochastic because of an ever-changing environment. Quantifying the emergent system behavior and general performance measures of such a system by analytical methods is challenging. In our work, we present an agent-based model for MUAV surveillance missions. This paper focuses on the overall system performance of cooperative UAVs performing forest fire surveillance. The principal theme is to present the effects of three behaviors on overall performance: (1) the area allocation and (2) dynamic coverage, and (3) the effects of forest density on team allocation. For area allocation, three behaviors are simulated: (1) randomized, (2) two-layer barrier sweep coverage, and (3) full sweep coverage. For dynamic coverage, the effects of communication and resource unavailability during the mission are studied by analyzing the agent’s downtime spent on refueling. Last, an extensive simulation is carried out on wildfire models with varying forest density. It is found that cooperative complete sweep coverage strategies perform better than the rest and the performance of the team is greatly affected by the forest density. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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29 pages, 3575 KiB  
Article
Land Registry Framework Based on Self-Sovereign Identity (SSI) for Environmental Sustainability
by Mohammed Shuaib, Noor Hafizah Hassan, Sahnius Usman, Shadab Alam, Surbhi Bhatia, Parul Agarwal and Sheikh Mohammad Idrees
Sustainability 2022, 14(9), 5400; https://doi.org/10.3390/su14095400 - 30 Apr 2022
Cited by 17 | Viewed by 4128
Abstract
Providing a system user with a unique and secure identity is a prerequisite for authentication and authorization aspects of a security system. It is generally understood that the existing digital identity systems store the identity details in centralized databases, and users store the [...] Read more.
Providing a system user with a unique and secure identity is a prerequisite for authentication and authorization aspects of a security system. It is generally understood that the existing digital identity systems store the identity details in centralized databases, and users store the identity details in centralized databases in which users do not have any control over them. These vulnerabilities in the traditional digital identities make them susceptible to various malicious assaults and modifications. Users’ personally identifiable information (PII) may leak through these identity solutions that can consequently affect other applications being used by the users, and they have no control over them. Land registration is a major domain of governance that defines civilians’ well-being and needs to be handled properly to avoid conflict and to support Environmental Sustainability. These traditional land registry applications also lack identity parameters due to weaknesses in identity solutions. A secure and reliable digital identity solution is the need of the hour. Self-sovereign identity (SSI), a new concept, is becoming more popular as a secure and reliable identity solution for users based on identity principles. SSI provides users with a way to control their personal information and consent for it to be used in various ways. In addition, the user’s identity details are stored in a decentralized manner, which helps to overcome the problems with digital identity solutions. This article reviews existing SSI solutions and analyzes them using SSI principles. It also assesses the SSI components required for constructing SSI frameworks that adhere to the SSI principles. Furthermore, it defines the procedures for establishing an SSI ecosystem, explores the laws governing digital identity that governments have adopted, and identifies SSI applications in several fields. Finally, a review of SSI applications in the domain of land registry systems is given in order to propose an SSI-based land registry framework for a secure and reliable land registry system. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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27 pages, 7442 KiB  
Article
Predictive Maintenance Planning for Industry 4.0 Using Machine Learning for Sustainable Manufacturing
by Mustufa Haider Abidi, Muneer Khan Mohammed and Hisham Alkhalefah
Sustainability 2022, 14(6), 3387; https://doi.org/10.3390/su14063387 - 14 Mar 2022
Cited by 40 | Viewed by 10132
Abstract
With the advent of the fourth industrial revolution, the application of artificial intelligence in the manufacturing domain is becoming prevalent. Maintenance is one of the important activities in the manufacturing process, and it requires proper attention. To decrease maintenance costs and to attain [...] Read more.
With the advent of the fourth industrial revolution, the application of artificial intelligence in the manufacturing domain is becoming prevalent. Maintenance is one of the important activities in the manufacturing process, and it requires proper attention. To decrease maintenance costs and to attain sustainable operational management, Predictive Maintenance (PdM) has become important in industries. The principle of PdM is forecasting the next failure; thus, the respective maintenance is scheduled before the predicted failure occurs. In the construction of maintenance management, facility managers generally employ reactive or preventive maintenance mechanisms. However, reactive maintenance does not have the ability to prevent failure and preventive maintenance does not have the ability to predict the future condition of mechanical, electrical, or plumbing components. Therefore, to improve the facilities’ lifespans, such components are repaired in advance. In this paper, a PdM planning model is developed using intelligent methods. The developed method involves five main phases: (a) data cleaning, (b) data normalization, (c) optimal feature selection, (d) prediction network decision-making, and (e) prediction. Initially, the data pertaining to PdM are subjected to data cleaning and normalization in order to arrange the data within a particular limit. Optimal feature selection is performed next, to reduce redundant information. Optimal feature selection is performed using a hybrid of the Jaya algorithm and Sea Lion Optimization (SLnO). As the prediction values differ in range, it is difficult for machine learning or deep learning face to provide accurate results. Thus, a support vector machine (SVM) is used to make decisions regarding the prediction network. The SVM identifies the network in which prediction can be performed for the concerned range. Finally, the prediction is accomplished using a Recurrent Neural Network (RNN). In the RNN, the weight is optimized using the hybrid J-SLnO. A comparative analysis demonstrates that the proposed model can efficiently predict the future condition of components for maintenance planning by using two datasets—aircraft engine and lithium-ion battery datasets. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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20 pages, 644 KiB  
Article
Entice to Trap: Enhanced Protection against a Rate-Aware Intelligent Jammer in Cognitive Radio Networks
by Khalid Ibrahim, Abdullah M. Alnajim, Aqdas Naveed Malik, Athar Waseem, Saleh Alyahya, Muhammad Islam and Sheroz Khan
Sustainability 2022, 14(5), 2957; https://doi.org/10.3390/su14052957 - 03 Mar 2022
Cited by 4 | Viewed by 1635
Abstract
Anti-jamming in cognitive radio networks (CRN) is mainly accomplished using machine learning techniques in the domains of frequency, coding, power and rate. Jamming is a major threat to CRN because it can cause severe performance damage such as network isolation, network application interruption [...] Read more.
Anti-jamming in cognitive radio networks (CRN) is mainly accomplished using machine learning techniques in the domains of frequency, coding, power and rate. Jamming is a major threat to CRN because it can cause severe performance damage such as network isolation, network application interruption and even physical damage to infrastructure simple radio devices. With the improvement in communication technologies, the capabilities of adversaries are also increased. The intelligent jammer knows the rate at which users transmit data, which is based on the attractiveness factor of each user. The higher the data rate for a secondary user, the more attractive it is to the rate-aware jammer. In this paper, we present a dummy user in the network as a honeypot of the jammer to get the jammer’s attention. A new anti-jamming deceiving theoretical method based on rate modifications is introduced to increase the bandwidth efficiency of the entire cognitive radio-based communication system. We employ a defensive anti-jamming deception mechanism of the Pseudo Secondary User (PSU) to as an entice to trap the attacker by providing thus enhanced protection for the rest of the network from the impact of the attacker. Our analytical simulation results show a significant improvement in performance using the proposed solution. The utility of the proposed intelligent anti-jamming algorithm lies in its applications to support the secondary wireless sensor nodes. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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14 pages, 1591 KiB  
Article
Federated Learning Approach to Protect Healthcare Data over Big Data Scenario
by Gaurav Dhiman, Sapna Juneja, Hamidreza Mohafez, Ibrahim El-Bayoumy, Lokesh Kumar Sharma, Maryam Hadizadeh, Mohammad Aminul Islam, Wattana Viriyasitavat and Mayeen Uddin Khandaker
Sustainability 2022, 14(5), 2500; https://doi.org/10.3390/su14052500 - 22 Feb 2022
Cited by 28 | Viewed by 4438
Abstract
The benefits and drawbacks of various technologies, as well as the scope of their application, are thoroughly discussed. The use of anonymity technology and differential privacy in data collection can aid in the prevention of attacks based on background knowledge gleaned from data [...] Read more.
The benefits and drawbacks of various technologies, as well as the scope of their application, are thoroughly discussed. The use of anonymity technology and differential privacy in data collection can aid in the prevention of attacks based on background knowledge gleaned from data integration and fusion. The majority of medical big data are stored on a cloud computing platform during the storage stage. To ensure the confidentiality and integrity of the information stored, encryption and auditing procedures are frequently used. Access control mechanisms are mostly used during the data sharing stage to regulate the objects that have access to the data. The privacy protection of medical and health big data is carried out under the supervision of machine learning during the data analysis stage. Finally, acceptable ideas are put forward from the management level as a result of the general privacy protection concerns that exist throughout the life cycle of medical big data throughout the industry. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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Review

Jump to: Research

31 pages, 3723 KiB  
Review
A Contemporary Review on Deep Learning Models for Drought Prediction
by Amogh Gyaneshwar, Anirudh Mishra, Utkarsh Chadha, P. M. Durai Raj Vincent, Venkatesan Rajinikanth, Ganapathy Pattukandan Ganapathy and Kathiravan Srinivasan
Sustainability 2023, 15(7), 6160; https://doi.org/10.3390/su15076160 - 03 Apr 2023
Cited by 5 | Viewed by 3515
Abstract
Deep learning models have been widely used in various applications, such as image and speech recognition, natural language processing, and recently, in the field of drought forecasting/prediction. These models have proven to be effective in handling large and complex datasets, and in automatically [...] Read more.
Deep learning models have been widely used in various applications, such as image and speech recognition, natural language processing, and recently, in the field of drought forecasting/prediction. These models have proven to be effective in handling large and complex datasets, and in automatically extracting relevant features for forecasting. The use of deep learning models in drought forecasting can provide more accurate and timely predictions, which are crucial for the mitigation of drought-related impacts such as crop failure, water shortages, and economic losses. This review provides information on the type of droughts and their information systems. A comparative analysis of deep learning models, related technology, and research tabulation is provided. The review has identified algorithms that are more pertinent than others in the current scenario, such as the Deep Neural Network, Multi-Layer Perceptron, Convolutional Neural Networks, and combination of hybrid models. The paper also discusses the common issues for deep learning models for drought forecasting and the current open challenges. In conclusion, deep learning models offer a powerful tool for drought forecasting, which can significantly improve our understanding of drought dynamics and our ability to predict and mitigate its impacts. However, it is important to note that the success of these models is highly dependent on the availability and quality of data, as well as the specific characteristics of the drought event. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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19 pages, 1007 KiB  
Review
Clustering, Routing, Scheduling, and Challenges in Bio-Inspired Parameter Tuning of Vehicular Ad Hoc Networks for Environmental Sustainability
by Christy Jackson Joshua, Prassanna Jayachandran, Abdul Quadir Md, Arun Kumar Sivaraman and Kong Fah Tee
Sustainability 2023, 15(6), 4767; https://doi.org/10.3390/su15064767 - 08 Mar 2023
Cited by 6 | Viewed by 2135
Abstract
Vehicular ad hoc networks (VANETs) are wireless networks of automotive nodes. Among the strategies used in VANETs to increase network connectivity are broadcast scheduling, data aggregation, and vehicular node clustering. In the context of extremely high node mobility and ambiguous vehicle distribution (on [...] Read more.
Vehicular ad hoc networks (VANETs) are wireless networks of automotive nodes. Among the strategies used in VANETs to increase network connectivity are broadcast scheduling, data aggregation, and vehicular node clustering. In the context of extremely high node mobility and ambiguous vehicle distribution (on the road), VANETs degrade in flexibility and quick topology, facing significant issues such as network physical layout construction and unstable connections. These challenges make it difficult for vehicle communication to be robust, reliable, and scalable, especially in urban traffic networks. Numerous research investigations have revealed a nearly optimal solution to various VANET difficulties through the application of techniques derived from nature and evolution. On the other hand, as key productivity sectors continue to demand more energy, sustainable and efficient ways of using non-renewable resources continue to be developed. With the help of information and communication technologies (ICT), parameter tuning approaches can reduce accident rates, improve mobility, and mitigate environmental impacts. In this article, we explore evolutionary algorithms to mobile ad hoc networks (MANETs), as well as vehicular ad hoc networks (VANETs). A discussion of three major categories of optimization is provided throughout the paper. There are several significant research works presented regarding parameter tuning in cluster formation, routing, and scheduling of broadcasts. Toward the end of the review, key challenges in VANET and MANET research are identified. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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21 pages, 1379 KiB  
Review
Blockchain for Internet of Underwater Things: State-of-the-Art, Applications, Challenges, and Future Directions
by Sweta Bhattacharya, Nancy Victor, Rajeswari Chengoden, Murugan Ramalingam, Govardanan Chemmalar Selvi, Praveen Kumar Reddy Maddikunta, Praveen Kumar Donta, Schahram Dustdar, Rutvij H. Jhaveri and Thippa Reddy Gadekallu
Sustainability 2022, 14(23), 15659; https://doi.org/10.3390/su142315659 - 24 Nov 2022
Cited by 18 | Viewed by 3078
Abstract
The Internet of Underwater Things (IoUT) has become widely popular in the past decade as it has huge prospects for the economy due to its applicability in various use cases such as environmental monitoring, disaster management, localization, defense, underwater exploration, and so on. [...] Read more.
The Internet of Underwater Things (IoUT) has become widely popular in the past decade as it has huge prospects for the economy due to its applicability in various use cases such as environmental monitoring, disaster management, localization, defense, underwater exploration, and so on. However, each of these use cases poses specific challenges with respect to security, privacy, transparency, and traceability, which can be addressed by the integration of blockchain with the IoUT. Blockchain is a Distributed Ledger Technology (DLT) that consists of series of blocks chained up in chronological order in a distributed network. In this paper, we present a first-of-its-kind survey on the integration of blockchain with the IoUT. This paper initially discusses the blockchain technology and the IoUT and points out the benefits of integrating blockchain technology with IoUT systems. An overview of various applications, the respective challenges, and the possible future directions of blockchain-enabled IoUT systems is also presented in this survey, and finally, the work sheds light on the critical aspects of IoUT systems and will enable researchers to address the challenges using blockchain technology. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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28 pages, 718 KiB  
Review
Smart Water Resource Management Using Artificial Intelligence—A Review
by Siva Rama Krishnan, M. K. Nallakaruppan, Rajeswari Chengoden, Srinivas Koppu, M. Iyapparaja, Jayakumar Sadhasivam and Sankaran Sethuraman
Sustainability 2022, 14(20), 13384; https://doi.org/10.3390/su142013384 - 17 Oct 2022
Cited by 28 | Viewed by 13837
Abstract
Water management is one of the crucial topics discussed in most of the international forums. Water harvesting and recycling are the major requirements to meet the global upcoming demand of the water crisis, which is prevalent. To achieve this, we need more emphasis [...] Read more.
Water management is one of the crucial topics discussed in most of the international forums. Water harvesting and recycling are the major requirements to meet the global upcoming demand of the water crisis, which is prevalent. To achieve this, we need more emphasis on water management techniques that are applied across various categories of the applications. Keeping in mind the population density index, there is a dire need to implement intelligent water management mechanisms for effective distribution, conservation and to maintain the water quality standards for various purposes. The prescribed work discusses about few major areas of applications that are required for efficient water management. Those are recent trends in wastewater recycle, water distribution, rainwater harvesting and irrigation management using various Artificial Intelligence (AI) models. The data acquired for these applications are purely unique and also differs by type. Hence, there is a dire need to use a model or algorithm that can be applied to provide solutions across all these applications. Artificial Intelligence (AI) and Deep Learning (DL) techniques along with the Internet of things (IoT) framework can facilitate in designing a smart water management system for sustainable water usage from natural resources. This work surveys various water management techniques and the use of AI/DL along with the IoT network and case studies, sample statistical analysis to develop an efficient water management framework. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Big Data Analytics for Environmental Sustainability)
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