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Sustainability
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Data Analytics and Predictive Analytics for Sustainable Development
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Data Analytics and Predictive Analytics for Sustainable Development

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Health, Well-Being and Sustainability".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 5308

Special Issue Editors

Dr. Mehrbakhsh Nilashi

E-Mail Website
Guest Editor
Centre for Global Sustainability Studies (CGSS), Universiti Sains Malaysia, 11800 USM Penang, Malaysia
Interests: machine learning; data analytics; health informatics; environmental informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Shahla Asadi

E-Mail Website
Guest Editor
Centre of Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia
Interests: green innovation; internet of things (IoT); cloud computing; information systems management; big data
Dr. Rabab Ali Abumalloh

E-Mail Website
Guest Editor
Computer Department, Applied College, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, Saudi Arabia
Interests: green technologies; artificial inteligence; recommender systems; tourism management; sustainable development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Sustainability is a pattern for considering environmental, societal, and economic factors in the quest for an enhanced living of communities. The crucial demand for sustainable development enforces extreme changes in the methods of planning and developing the industry and logistics schemes. These changing requirements have emerged due to multiple agreed on and continuously rising reasons: environmental issues, reducing non-renewable resources, firm regulations, excessive energy charges, and growing customer interest in eco-friendly items, etc. General sustainability in the manufacturing sector must be considered while addressing the exceptional degrees of worldwide competition. Among all scenarios that depend on innovations, big data analytics present a penetrative route that can address sustainability-related emerging problems.

The volume of data created daily, the speed of data creation, and the increasing demand for data storage and processing have raised significant challenges for researchers worldwide. Conceptual and empirical studies considering the role of big data and predictive analytics are still shattered. Comparing and assembling the outcomes of these studies to reach insightful findings is challenging. Sustainability analysis and planning present a handful of answers to address emerging problems by allowing the use of modeling, planning, simulation, controlling, and optimization in order to design more sustainable items and procedures. In the age of digitalization, data analytics and related methods are advancing the wide area of societal, economic, and environmental aspects of humans’ lives.

This Special Issue will entail studies that investigate the data analytics and predictive analytic methods related to the multidimensional aspects of sustainability. Through a set of studies, it investigates beneficial methods for big data analysis, predictive analysis, decision making, and research directions towards addressing the current problems related to sustainability. Policy designing should incorporate advanced predictive analytics techniques towards the acceleration of the basic amendments for a sustainable way of living. The Special Issue “Data Analytics and Predictive Analytics for Sustainable Development” aims to present practical solutions answering to current issues related to sustainability such as climate change, green technology, smart cities, and environmental problems by focusing on the promising methods of data analytics and predictive analytics. Specifically, the Special Issue aims to present the most novel accomplishments and advancements in big data discovery and investigation in the context of sustainability. Both theoretical types of research and practical applications are encouraged for submission. Overall, this Special Issue focuses on, but is not limited to, the following topics:

  • Environmental sustainability
  • Big data processing and analysis for environment-related issues
  • Big data information security for sustainability
  • Big data for sustainable tourism
  • Big data for green supply chains
  • Big data adoption and management
  • Big data for supply chain sustainability
  • Big data toward green applications
  • Big data for smart cities
  • Big data analytics for smart buildings
  • Business analytics and decision support
  • Business model innovation for sustainability and predictive analytics
  • Computational intelligence systems for sustainability
  • Cloud computing platform and big data mining
  • Complex information systems for sustainability
  • Data-driven approaches for sustainability
  • Internet of things (IoT) technologies for sustainability
  • Knowledge-based systems for sustainability
  • Large-scale sustainable infrastructure
  • Predictive analytics in green information systems

Dr. Mehrbakhsh Nilashi
Dr. Shahla Asadi
Mrs. Rabab Ali Abumalloh
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

  • big data
  • predictive analytics
  • data analytics
  • social sustainability
  • environmental sustainability, data-driven approaches, green applications

Published Papers (2 papers)

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20 pages, 2634 KiB  
Article
Cluster Forecasting of Corruption Using Nonlinear Autoregressive Models with Exogenous Variables (NARX)—An Artificial Neural Network Analysis
by SeyedAli Ghahari, Cesar Queiroz, Samuel Labi and Sue McNeil
Sustainability 2021, 13(20), 11366; https://doi.org/10.3390/su132011366 - 14 Oct 2021
Cited by 4 | Viewed by 1408
Abstract
Any effort to combat corruption can benefit from an examination of past and projected worldwide trends. In this paper, we forecast the level of corruption in countries by integrating artificial neural network modeling and time series analysis. The data were obtained from 113 [...] Read more.
Any effort to combat corruption can benefit from an examination of past and projected worldwide trends. In this paper, we forecast the level of corruption in countries by integrating artificial neural network modeling and time series analysis. The data were obtained from 113 countries from 2007 to 2017. The study is carried out at two levels: (a) the global level, where all countries are considered as a monolithic group; and (b) the cluster level, where countries are placed into groups based on their development-related attributes. For each cluster, we use the findings from our previous study on the cluster analysis of global corruption using machine learning methods that identified the four most influential corruption factors, and we use those as independent variables. Then, using the identified influential factors, we forecast the level of corruption in each cluster using nonlinear autoregressive recurrent neural network models with exogenous inputs (NARX), an artificial neural network technique. The NARX models were developed for each cluster, with an objective function in terms of the Corruption Perceptions Index (CPI). For each model, the optimal neural network is determined by fine-tuning the hyperparameters. The analysis was repeated for all countries as a single group. The accuracy of the models is assessed by comparing the mean square errors (MSEs) of the time series models. The results suggest that the NARX artificial neural network technique yields reliable future values of CPI globally or for each cluster of countries. This can assist policymakers and organizations in assessing the expected efficacies of their current or future corruption control policies from a global perspective as well as for groups of countries. Full article
(This article belongs to the Special Issue Data Analytics and Predictive Analytics for Sustainable Development)
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24 pages, 9043 KiB  
Article
Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART)
by Mehrbakhsh Nilashi, Shahla Asadi, Rabab Ali Abumalloh, Sarminah Samad, Fahad Ghabban, Eko Supriyanto and Reem Osman
Sustainability 2021, 13(7), 3870; https://doi.org/10.3390/su13073870 - 31 Mar 2021
Cited by 7 | Viewed by 2409
Abstract
This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method [...] Read more.
This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method in this study was developed using cluster analysis and prediction learning techniques. A Self-Organizing Map (SOM) was applied for data clustering, while Classification and Regression Trees (CART) were applied to assess sustainability performance. The proposed method was evaluated through Sustainability Assessment by Fuzzy Evaluation (SAFE) dataset, which comprises various indicators of sustainability performance in 128 countries. Eight clusters from the data were found through the SOM clustering technique. A prediction model was found in each cluster through the CART technique. In addition, an ensemble of CART was constructed in each cluster of SOM to increase the prediction accuracy of CART. All prediction models were assessed through the adjusted coefficient of determination approach. The results demonstrated that the prediction accuracy values were high in all CART models. The results indicated that the method developed by ensembles of CART and clustering provide higher prediction accuracy than individual CART models. The main advantage of integrating the proposed method is its ability to automate decision rules from big data for prediction models. The method proposed in this study could be implemented as an effective tool for sustainability performance assessment. Full article
(This article belongs to the Special Issue Data Analytics and Predictive Analytics for Sustainable Development)
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