Special Issue "AI and Deep Learning Applications for Water Management"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water-Energy Nexus".

Deadline for manuscript submissions: closed (29 August 2022) | Viewed by 15177

Special Issue Editors

Dr. Celestine Iwendi
E-Mail Website
Guest Editor
School of Creative Technologies, University of Bolton, Bolton BL3 5AB, UK
Interests: artificial intelligence; machine learning; internet of things; blockchain; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals
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,

Water is an essential source for survival for humans, animals, and plants. Due to rise in population of humans and also due to industrialization, water sources are being depleted very quickly. To minimize water depletion, efffective water management is the need of the hour. Normally, governments supply water to their citizens. A continous supply of water is not required for the household as the water may be wasted by suppplying the water continuously. Each household may use water at a specific time. Machine learning and deep learning algorithms can use the data regarding the water usage patterns/records from the government to get trained on the usage patterns, so that these algorithms may be used to classify the areas according to their peak usage time. If these patterns are successfully identified, then the Government bodies can understand at what time and what quantity of the water is required for a particular area, through which we can reduce the waste of water. The aim of this Special Issue is to solicit quality research works on effective water management using machine learning and deep learning techniques.

Dr. Celestine Iwendi
Prof. Dr. Thippa Reddy Gadekallu
Guest Editors

Manuscript Submission Information

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Keywords

  • machine learning
  • deep learning
  • water management
  • smart city
  • water usage patterns

Published Papers (14 papers)

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Research

Communication
Unsupervised Representation Learning of GRACE Improves Groundwater Predictions
Water 2022, 14(19), 2947; https://doi.org/10.3390/w14192947 - 20 Sep 2022
Viewed by 277
Abstract
Groundwater is a crucial source of the world’s drinking and irrigation water. Nonetheless, it is being rapidly depleted in many parts of the world. To enact policy decisions to preserve this precious resource, policymakers need real-time data on the groundwater levels in their [...] Read more.
Groundwater is a crucial source of the world’s drinking and irrigation water. Nonetheless, it is being rapidly depleted in many parts of the world. To enact policy decisions to preserve this precious resource, policymakers need real-time data on the groundwater levels in their local area. However, groundwater monitoring wells are costly and scarce in supply. The use of satellite imagery is a promising alternative with its ability to provide continuous information over a large area. Machine learning has also emerged as an alternative to computationally intensive physics-based models. However, advancements in machine learning such as unsupervised learning methods have never been translated to groundwater modeling. Thus, in this paper, learned representations were generated for the GRACE satellite for the first time. When used as an input to groundwater prediction models, the learned representations reduce the root mean square error (RMSE) by up to 19% and improve the Nash–Sutcliffe efficiency (NSE) by up to 8x compared to traditional satellite data inputs at three different spatial scales: national, state, and county. The learned representations are able to discern fine-grained patterns from the coarse satellite data, globally downscaling the GRACE satellite. Crucially, the globally trained representations have the potential to improve the performance of virtually every machine learning-based groundwater prediction model. With accurate measurements, local officials are empowered to make proactive decisions to ensure the stability of their region’s water. Full article
(This article belongs to the Special Issue AI and Deep Learning Applications for Water Management)
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Article
Optimized Scenario for Estimating Suspended Sediment Yield Using an Artificial Neural Network Coupled with a Genetic Algorithm
Water 2022, 14(18), 2815; https://doi.org/10.3390/w14182815 - 09 Sep 2022
Viewed by 473
Abstract
Rivers are the agents on earth and act as the main pathways for transporting the continental weathered materials into the sea. The estimation of suspended sediment yield (SSY) is important in the design, planning and management of water resources. The SSY depends on [...] Read more.
Rivers are the agents on earth and act as the main pathways for transporting the continental weathered materials into the sea. The estimation of suspended sediment yield (SSY) is important in the design, planning and management of water resources. The SSY depends on many factors and their interrelationships, which are very nonlinear and complex. The traditional approaches are unable to solve these complex nonlear processes of SSY. Thus, the development of a reliable and accurate model for estimating the SSY is essential. The goal of this research was to develop a single hybrid artificial intelligence model, which is a hybridization of the artificial neural network (ANN) and genetic algorithm (GA) (ANN-GA) for the estimation of SSY in the Mahanadi River (MR), India, by combining data from 11-gauge stations into a single hybrid generalized model and applying it to every gauging station for estimating the SSY. All parameters of the ANN model were optimized automatically and simultaneously using GA to estimate the SSY. The proposed model was developed considering the temporal monthly hydro-climatic data, such as temperature (T), rainfall (RF), water discharge (Q) and SSY and spatial data, including the rock type (RT), catchment area (CA) and relief (R), of all 11 gauging stations in the MR. The performances of the conventional sediment rating curve (SRC), ANN and multiple linear regression (MLR) were compared with the hybrid ANN-GA model. It was noticed that the ANN-GA model provided with greatest coefficient of correlation (0.8710) and lowest root mean square error (0.0088) values among all comparative SRC, ANN and MLR. Thus, the proposed ANN-GA is most appropriate model compared to other examined models for estimating SSY in the MR Basin, India, particularly at the Tikarapara measuring station. If no measures of SSY are available in the MR, then the modelling approach could be used to estimate SSY at ungauged or gauge stations in the MR Basin. Full article
(This article belongs to the Special Issue AI and Deep Learning Applications for Water Management)
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Article
Neural Structures to Predict River Stages in Heavily Urbanized Catchments
Water 2022, 14(15), 2330; https://doi.org/10.3390/w14152330 - 27 Jul 2022
Viewed by 522
Abstract
Accurate flow forecasting may support responsible institutions in managing river systems and limiting damages due to high water levels. Machine-learning models are known to describe many nonlinear hydrological phenomena, but up to now, they have mainly provided a single future value with a [...] Read more.
Accurate flow forecasting may support responsible institutions in managing river systems and limiting damages due to high water levels. Machine-learning models are known to describe many nonlinear hydrological phenomena, but up to now, they have mainly provided a single future value with a fixed information structure. This study trains and tests multi-step deep neural networks with different inputs to forecast the water stage of two sub-alpine urbanized catchments. They prove effective for one hour ahead flood stage values and occurrences. Convolutional neural networks (CNNs) perform better when only past information on the water stage is used. Long short-term memory nets (LSTMs) are more suited to exploit the data coming from the rain gauges. Predicting a set of water stages over the following hour rather than just a single future value may help concerned agencies take the most urgent actions. The paper also shows that the architecture developed for one catchment can be adapted to similar ones maintaining high accuracy. Full article
(This article belongs to the Special Issue AI and Deep Learning Applications for Water Management)
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Article
ECDSA-Based Water Bodies Prediction from Satellite Images with UNet
Water 2022, 14(14), 2234; https://doi.org/10.3390/w14142234 - 15 Jul 2022
Cited by 3 | Viewed by 638
Abstract
The detection of water bodies from satellite images plays a vital role in research development. It has a wide range of applications such as the prediction of natural disasters and detecting drought and flood conditions. There were few existing applications that focused on [...] Read more.
The detection of water bodies from satellite images plays a vital role in research development. It has a wide range of applications such as the prediction of natural disasters and detecting drought and flood conditions. There were few existing applications that focused on detecting water bodies that are becoming extinct in nature. The dataset to train this deep learning model is taken from Kaggle. It has two classes, namely water bodies and masks. There is a total of 2841 sentinel-2 satellite images with corresponding 2841 masks. Additionally, the present work focuses on using UNet, Tensorflow to detect the water bodies. It uses a Nadam optimizer to reduce the losses. It also finds best-optimized parameters for the activation function, a number of nodes in each layer. This proposed model achieves integrity by embedding a security feature Elliptic Curve Digital Signature Algorithm (ECDSA). It generates a digital signature for the predicted area of water bodies which helps to secure the key and the detected water bodies while transmitting in a channel. Thus, the proposed model ensures the performance accuracy of 94% which can also work the same for edge detection, detection in blurred and low-resolution images. The model is highly robust. Full article
(This article belongs to the Special Issue AI and Deep Learning Applications for Water Management)
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Article
Capability and Robustness of Novel Hybridized Artificial Intelligence Technique for Sediment Yield Modeling in Godavari River, India
Water 2022, 14(12), 1917; https://doi.org/10.3390/w14121917 - 14 Jun 2022
Cited by 3 | Viewed by 885
Abstract
Suspended sediment yield (SSY) prediction plays a crucial role in the planning of water resource management and design. Accurate sediment prediction using conventional models is very difficult due to many complex processes. We developed a fully automatic highly generalized accurate and robust artificial [...] Read more.
Suspended sediment yield (SSY) prediction plays a crucial role in the planning of water resource management and design. Accurate sediment prediction using conventional models is very difficult due to many complex processes. We developed a fully automatic highly generalized accurate and robust artificial intelligence models for SSY prediction in Godavari River Basin, India. The genetic algorithm (GA), hybridized with an artificial neural network (ANN) (GA-ANN), is a suitable artificial intelligence model for SSY prediction. The GA is used to concurrently optimize all ANN’s parameters. The GA-ANN was developed using daily water discharge, with water level as the input data to estimate the daily SSY at Polavaram, which is the farthest gauging station in the downstream of the Godavari River Basin. The performances of the GA-ANN model were evaluated by comparing with ANN, sediment rating curve (SRC) and multiple linear regression (MLR) models. It is observed that the GA-ANN contains the highest correlation coefficient (0.927) and lowest root mean square error (0.053) along with lowest biased (0.020) values among all the comparative models. The GA-ANN model is the most suitable substitute over traditional models for SSY prediction. The hybrid GA-ANN can be recommended for estimating the SSY due to comparatively superior performance and simplicity of applications. Full article
(This article belongs to the Special Issue AI and Deep Learning Applications for Water Management)
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Article
Urban River Dissolved Oxygen Prediction Model Using Machine Learning
Water 2022, 14(12), 1899; https://doi.org/10.3390/w14121899 - 13 Jun 2022
Viewed by 581
Abstract
This study outlines the preliminary stages of the development of an algorithm to predict the optimal WQ of the Hwanggujicheon Stream. In the first stages, we used the AdaBoost algorithm model to predict the state of WQ, using data from the open artificial [...] Read more.
This study outlines the preliminary stages of the development of an algorithm to predict the optimal WQ of the Hwanggujicheon Stream. In the first stages, we used the AdaBoost algorithm model to predict the state of WQ, using data from the open artificial intelligence (AI) hub. The AdaBoost algorithm has excellent predictive performance and model suitability and was selected for random forest and gradient boosting (GB)-based boosting models. To predict the optimized WQ, we selected pH, SS, water temperature, total nitrogen(TN), dissolved total phosphorus(DTP), NH3-N, chemical oxygen demand (COD), dissolved total nitrogen (DTN), and NO3-N as the input variables of the AdaBoost model. Dissolved oxygen (DO) was used as the target variable. Third, an algorithm showing excellent predictive power was selected by analyzing the prediction accuracy according to the input variable by using the random forest or GB series algorithm in the initial model. Finally, the performance evaluation of the ultimately developed predictive model demonstrated that RMS was 0.015, MAE was 0.009, and R2 was 0.912. The coefficient of the variation of the root mean square error (CVRMSE) was 17.404. R2 0.912 and CVRMSE were 17.404, indicating that the predictive model developed meets the criteria of ASHRAE Guideline 14. It is imperative that government and administrative agencies have access to effective tools to assess WQ and pollution levels in their local bodies of water. Full article
(This article belongs to the Special Issue AI and Deep Learning Applications for Water Management)
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Article
Probabilistic Framework Allocation on Underwater Vehicular Systems Using Hydrophone Sensor Networks
Water 2022, 14(8), 1292; https://doi.org/10.3390/w14081292 - 15 Apr 2022
Cited by 1 | Viewed by 489
Abstract
This article emphasis the importance of constructing an underwater vehicle monitoring system to solve various issues that are related to deep sea explorations. For solving the issues, conventional methods are not implemented, whereas a new underwater vehicle is introduced which acts as a [...] Read more.
This article emphasis the importance of constructing an underwater vehicle monitoring system to solve various issues that are related to deep sea explorations. For solving the issues, conventional methods are not implemented, whereas a new underwater vehicle is introduced which acts as a sensing device and monitors the ambient noise in the system. However, the fundamentals of creating underwater vehicles have been considered from conventional systems and the new formulations are generated. This innovative sensing device will function based on the energy produced by the solar cells which will operate for a short period of time under the water where low parametric units are installed. In addition, the energy consumed for operating a particular unit is much lesser and this results in achieving high reliability using a probabilistic path finding algorithm. Further, two different application segments have been solved using the proposed formulations including the depth of monitoring the ocean. To validate the efficiency of the proposed method, comparisons have been made with existing methods in terms of navigation output units, rate of decomposition for solar cells, reliability rate, and directivity where the proposed method proves to be more efficient for an average percentile of 64%. Full article
(This article belongs to the Special Issue AI and Deep Learning Applications for Water Management)
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Article
A Machine-Learning Approach for Prediction of Water Contamination Using Latitude, Longitude, and Elevation
Water 2022, 14(5), 728; https://doi.org/10.3390/w14050728 - 24 Feb 2022
Cited by 2 | Viewed by 884
Abstract
One of the significant issues that the world has faced in recent decades has been the estimation of water quality and location where safe drinking water is available. Due to the unexpected nature of the mode of water contamination, it is not easy [...] Read more.
One of the significant issues that the world has faced in recent decades has been the estimation of water quality and location where safe drinking water is available. Due to the unexpected nature of the mode of water contamination, it is not easy to analyze the quality and maintain it. Some machine-learning techniques are used for predicting contaminating factors but there is no technique that can predict the contamination using latitude, longitude, and elevation. The main aim of this paper is to put factors such as water body location and elevation, which are used as inputs, into the different machine-learning techniques that predict the contamination. The results are reviewed and analyzed according to groundwater contamination and the chemical composition of the groundwater location. Non-changeable factors such as latitude, longitude, and elevation are used to predict pH, temperature, turbidity, dissolved oxygen hardness, chlorides, alkalinity, and chemical oxygen demand. Such a study has not been conducted in the past where location-based factors are used to predict the water contamination of any area. This research focuses on creating a relationship between the location base factors affecting the water contamination in a given area. Full article
(This article belongs to the Special Issue AI and Deep Learning Applications for Water Management)
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Article
Hash-Based Deep Learning Approach for Remote Sensing Satellite Imagery Detection
Water 2022, 14(5), 707; https://doi.org/10.3390/w14050707 - 23 Feb 2022
Cited by 14 | Viewed by 1192
Abstract
Ship detection plays a crucial role in marine security in remote sensing imagery. This paper discusses about a deep learning approach to detect the ships from satellite imagery. The model developed in this work achieves integrity by the inclusion of hashing. This model [...] Read more.
Ship detection plays a crucial role in marine security in remote sensing imagery. This paper discusses about a deep learning approach to detect the ships from satellite imagery. The model developed in this work achieves integrity by the inclusion of hashing. This model employs a supervised image classification technique to classify images, followed by object detection using You Only Look Once version 3 (YOLOv3) to extract features from deep CNN. Semantic segmentation and image segmentation is done to identify object category of each pixel using class labels. Then, the concept of hashing using SHA-256 is applied in conjunction with the ship count and location of bounding box in satellite image. The proposed model is tested on a Kaggle Ships dataset, which consists of 231,722 images. A total of 70% of this data is used for training, and the 30% is used for testing. To add security to images with detected ships, the model is enhanced by hashing using SHA-256 algorithm. Using SHA-256, which is a one-way hash, the data are split up into blocks of 64 bytes. The input data to the hash function are both the ship count and bounding box location. The proposed model achieves integrity by using SHA-256. This model allows secure transmission of highly confidential images that are tamper-proof. Full article
(This article belongs to the Special Issue AI and Deep Learning Applications for Water Management)
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Article
Water Level Forecasting Using Spatiotemporal Attention-Based Long Short-Term Memory Network
Water 2022, 14(4), 612; https://doi.org/10.3390/w14040612 - 17 Feb 2022
Cited by 2 | Viewed by 1362
Abstract
Bangladesh is in the floodplains of the Ganges, Brahmaputra, and Meghna River delta, crisscrossed by an intricate web of rivers. Although the country is highly prone to flooding, the use of state-of-the-art deep learning models in predicting river water levels to aid flood [...] Read more.
Bangladesh is in the floodplains of the Ganges, Brahmaputra, and Meghna River delta, crisscrossed by an intricate web of rivers. Although the country is highly prone to flooding, the use of state-of-the-art deep learning models in predicting river water levels to aid flood forecasting is underexplored. Deep learning and attention-based models have shown high potential for accurately forecasting floods over space and time. The present study aims to develop a long short-term memory (LSTM) network and its attention-based architectures to predict flood water levels in the rivers of Bangladesh. The models developed in this study incorporated gauge-based water level data over 7 days for flood prediction at Dhaka and Sylhet stations. This study developed five models: artificial neural network (ANN), LSTM, spatial attention LSTM (SALSTM), temporal attention LSTM (TALSTM), and spatiotemporal attention LSTM (STALSTM). The multiple imputation by chained equations (MICE) method was applied to address missing data in the time series analysis. The results showed that the use of both spatial and temporal attention together increases the predictive performance of the LSTM model, which outperforms other attention-based LSTM models. The STALSTM-based flood forecasting system, developed in this study, could inform flood management plans to accurately predict floods in Bangladesh and elsewhere. Full article
(This article belongs to the Special Issue AI and Deep Learning Applications for Water Management)
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Article
Underwater Fish Detection and Counting Using Mask Regional Convolutional Neural Network
Water 2022, 14(2), 222; https://doi.org/10.3390/w14020222 - 12 Jan 2022
Cited by 5 | Viewed by 1128
Abstract
Fish production has become a roadblock to the development of fish farming, and one of the issues encountered throughout the hatching process is the counting procedure. Previous research has mainly depended on the use of non-machine learning-based and machine learning-based counting methods and [...] Read more.
Fish production has become a roadblock to the development of fish farming, and one of the issues encountered throughout the hatching process is the counting procedure. Previous research has mainly depended on the use of non-machine learning-based and machine learning-based counting methods and so was unable to provide precise results. In this work, we used a robotic eye camera to capture shrimp photos on a shrimp farm to train the model. The image data were classified into three categories based on the density of shrimps: low density, medium density, and high density. We used the parameter calibration strategy to discover the appropriate parameters and provided an improved Mask Regional Convolutional Neural Network (Mask R-CNN) model. As a result, the enhanced Mask R-CNN model can reach an accuracy rate of up to 97.48%. Full article
(This article belongs to the Special Issue AI and Deep Learning Applications for Water Management)
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Article
Interfacial Friction Prediction in a Vertical Annular Two-Phase Flow Based on Support Vector Regression Machine
Water 2021, 13(24), 3609; https://doi.org/10.3390/w13243609 - 15 Dec 2021
Cited by 1 | Viewed by 779
Abstract
Accurate prediction of interfacial friction factor is critical for calculation of pressure drop and investigation of flow mechanism of vertical annular two-phase flows. Theoretical models of interfacial friction factor based on physical insight have been developed; however, these are inconvenient in engineering practice [...] Read more.
Accurate prediction of interfacial friction factor is critical for calculation of pressure drop and investigation of flow mechanism of vertical annular two-phase flows. Theoretical models of interfacial friction factor based on physical insight have been developed; however, these are inconvenient in engineering practice as too many parameters need to be measured. Although many researchers have proposed various empirical correlations to improve computation efficiency, there is no generally accepted simple formula. In this study, an efficient prediction model based on support vector regression machine (SVR) is proposed. Through sensitivity analysis, five factors are determined as the input parameters to train the SVR model, relative liquid film thickness, liquid Reynolds number, gas Reynolds number, liquid Froude number and gas Froude number. The interfacial friction factor is chosen as the output parameter to check the overall performance of the model. With the help of particle swarm algorithm, the optimization process is accelerated considerably, and the optimal model is obtained through iterations. Compared with other correlations, the optimal model shows the lowest average absolute error (AAE of 0.0004), lowest maximum absolute error (MAE of 0.006), lowest root mean square error (RMSE of 0.00076) and highest correlation factor (r of 0.995). The analysis using various data in the literature demonstrates its accuracy and stability in interfacial friction prediction. In summary, the proposed machine learning model is effective and can be applied to a wider range of conditions for vertical annular two-phase flows. Full article
(This article belongs to the Special Issue AI and Deep Learning Applications for Water Management)
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Article
Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light
Water 2021, 13(23), 3470; https://doi.org/10.3390/w13233470 - 06 Dec 2021
Cited by 11 | Viewed by 1102
Abstract
A lack of adequate consideration of underwater image enhancement gives room for more research into the field. The global background light has not been adequately addressed amid the presence of backscattering. This paper presents a technique based on pixel differences between global and [...] Read more.
A lack of adequate consideration of underwater image enhancement gives room for more research into the field. The global background light has not been adequately addressed amid the presence of backscattering. This paper presents a technique based on pixel differences between global and local patches in scene depth estimation. The pixel variance is based on green and red, green and blue, and red and blue channels besides the absolute mean intensity functions. The global background light is extracted based on a moving average of the impact of suspended light and the brightest pixels within the image color channels. We introduce the block-greedy algorithm in a novel Convolutional Neural Network (CNN) proposed to normalize different color channels’ attenuation ratios and select regions with the lowest variance. We address the discontinuity associated with underwater images by transforming both local and global pixel values. We minimize energy in the proposed CNN via a novel Markov random field to smooth edges and improve the final underwater image features. A comparison of the performance of the proposed technique against existing state-of-the-art algorithms using entropy, Underwater Color Image Quality Evaluation (UCIQE), Underwater Image Quality Measure (UIQM), Underwater Image Colorfulness Measure (UICM), and Underwater Image Sharpness Measure (UISM) indicate better performance of the proposed approach in terms of average and consistency. As it concerns to averagely, UICM has higher values in the technique than the reference methods, which explainsits higher color balance. The μ values of UCIQE, UISM, and UICM of the proposed method supersede those of the existing techniques. The proposed noted a percent improvement of 0.4%, 4.8%, 9.7%, 5.1% and 7.2% in entropy, UCIQE, UIQM, UICM and UISM respectively compared to the best existing techniques. Consequently, dehazed images have sharp, colorful, and clear features in most images when compared to those resulting from the existing state-of-the-art methods. Stable σ values explain the consistency in visual analysis in terms of sharpness of color and clarity of features in most of the proposed image results when compared with reference methods. Our own assessment shows that only weakness of the proposed technique is that it only applies to underwater images. Future research could seek to establish edge strengthening without color saturation enhancement. Full article
(This article belongs to the Special Issue AI and Deep Learning Applications for Water Management)
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Article
Deep Learning Based Filtering Algorithm for Noise Removal in Underwater Images
Water 2021, 13(19), 2742; https://doi.org/10.3390/w13192742 - 02 Oct 2021
Cited by 6 | Viewed by 1179
Abstract
Under-water sensing and image processing play major roles in oceanic scientific studies. One of the related challenges is that the absorption and scattering of light in underwater settings degrades the quality of the imaging. The major drawbacks of underwater imaging are color distortion, [...] Read more.
Under-water sensing and image processing play major roles in oceanic scientific studies. One of the related challenges is that the absorption and scattering of light in underwater settings degrades the quality of the imaging. The major drawbacks of underwater imaging are color distortion, low contrast, and loss of detail (especially edge information). The paper proposes a method to address these issues by de-noising and increasing the resolution of the image using a model network trained on similar data. The network extracts frames from a video and filters them with a trigonometric–Gaussian filter to eliminate the noise in the image. It then applies contrast limited adaptive histogram equalization (CLAHE) to improvise the image contrast, and finally enhances the image resolution. Experimental results show that the proposed method could effectively produce enhanced images from degraded underwater images. Full article
(This article belongs to the Special Issue AI and Deep Learning Applications for Water Management)
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