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Water
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Water Resources Management: Advances in Machine Learning Approaches
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Water Resources Management: Advances in Machine Learning Approaches

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management, Policy and Governance".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 24167

Special Issue Editor

Prof. Dr. Aris Psilovikos

E-Mail Website
Guest Editor
Sustainable Water Resources Management, Department of Ichthyology and Aquatic Environment, School of Agricultural Sciences, University of Thessaly, Odos Fytokou, 38446 N. Ionia Magnisias, Greece
Interests: water resources simulation, optimization and management; water quality monitoring, simulation and management; temporal and spatial analysis of water quality and quantity parameters; water balance in catchment areas; erosion, floods and sedimentation in catchment areas; artificial neural networks (ANN); Geographic Information System (GIS); Remote Sensing (RS)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues

Water resources management at the catchment level is a scientific discipline with great environmental importance. It is a multidisciplinary issue which has prevailed from the cooperation of a wide range of scientists, such as engineers, Earth scientists, agronomists, environmentalists, biologists, and economists. The target is the optimal distribution of limited water resources and the preservation of acceptable levels of water quality, in such a way that all the users’ needs in domestic, agricultural, industrial and ecological uses are satisfied with the least controversy and conflicts.

In order to achieve operational and efficient water management, we need to have reliable monitoring time-series data and the appropriate tools for their processing.

Machine learning approaches are a very powerful tool for the simulation, prediction, optimization, assessment and management of catchment water resources. For many decades, a high number of both deterministic and stochastic models for the simulation and optimization of catchment water resources have been very successfully applied. Machine learning approaches are the first step of artificial intelligence, and can give more integrated answers to both quantitative and qualitative water management problems at the catchment level:

a) Water quantity management: evapotranspiration models; water balance models; land cover and land use; agricultural, domestic, industrial and environmental use of water.

b) Water quality management: water temperature; dissolved oxygen; chlorophyll-a; electrical conductivity of water; eutrophication indexes; rivers, lakes, wetlands, deltas, internal and transitional waters.

Prof. Dr. Aris Psilovikos
Guest Editor

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. Water 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 2600 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

  • catchment area
  • water resources management
  • water quality management
  • machine learning approaches
  • water balance
  • eutrophication
  • monitoring

Published Papers (7 papers)

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Research

16 pages, 5566 KiB  
Article
Deep Learning in Water Resources Management: Τhe Case Study of Kastoria Lake in Greece
by Lina Karamoutsou and Aris Psilovikos
Water 2021, 13(23), 3364; https://doi.org/10.3390/w13233364 - 28 Nov 2021
Cited by 12 | Viewed by 2564
Abstract
The effects of climate change on water resources management have drawn worldwide attention. Water quality predictions that are both reliable and precise are critical for an effective water resources management. Although nonlinear biological and chemical processes occurring in a lake make prediction complex, [...] Read more.
The effects of climate change on water resources management have drawn worldwide attention. Water quality predictions that are both reliable and precise are critical for an effective water resources management. Although nonlinear biological and chemical processes occurring in a lake make prediction complex, advanced techniques are needed to develop reliable models and effective management systems. Artificial intelligence (AI) is one of the most recent methods for modeling complex structures. The applications of machine learning (ML), as a part of AI, in hydrology and water resources management have been increasing in recent years. In this paper, the ability of deep neural networks (DNNs) to predict the quality parameter of dissolved oxygen (DO), in Lake Kastoria, Greece, is tested. The available dataset from 11 November 2015, to 15 March 2018, on an hourly basis, from four telemetric stations located in the study area consists of (1) Chl-a (μg/L), (2) pH, (3) temperature—Tw (°C), (4) conductivity (μS/cm), (5) turbidity (NTU), (6) ammonia (NH4, mg/L), (7) nitrate nitrogen (N–NO3, mg/L), and (8) dissolved oxygen (DO) (mg/L). Feed-forward deep neural networks (FF-DNNs) of DO, with different structures, are tested for all stations. All the well-trained DNNs give satisfactory results. The optimal selected FF-DNNs of DO for each station with a high efficiency (NSE > 0.89 for optimal selected structures/station) constitute a good choice for modeling dissolved oxygen. Moreover, they provide information in real time and comprise a powerful decision support system (DSS) for preventing accidental and emergency conditions that may arise from both natural and anthropogenic hazards. Full article
(This article belongs to the Special Issue Water Resources Management: Advances in Machine Learning Approaches)
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18 pages, 4872 KiB  
Article
Optimising the Workflow for Fish Detection in DIDSON (Dual-Frequency IDentification SONar) Data with the Use of Optical Flow and a Genetic Algorithm
by Triantafyllia-Maria Perivolioti, Michal Tušer, Dimitrios Terzopoulos, Stefanos P. Sgardelis and Ioannis Antoniou
Water 2021, 13(9), 1304; https://doi.org/10.3390/w13091304 - 07 May 2021
Cited by 5 | Viewed by 2367
Abstract
DIDSON acoustic cameras provide a way to collect temporally dense, high-resolution imaging data, similar to videos. Detection of fish targets on those videos takes place in a manual or semi-automated manner, typically assisted by specialised software. Exploiting the visual nature of the recordings, [...] Read more.
DIDSON acoustic cameras provide a way to collect temporally dense, high-resolution imaging data, similar to videos. Detection of fish targets on those videos takes place in a manual or semi-automated manner, typically assisted by specialised software. Exploiting the visual nature of the recordings, tools and techniques from the field of computer vision can be applied in order to facilitate the relatively involved workflows. Furthermore, machine learning techniques can be used to minimise user intervention and optimise for specific detection and tracking scenarios. This study explored the feasibility of combining optical flow with a genetic algorithm, with the aim of automating motion detection and optimising target-to-background segmentation (masking) under custom criteria, expressed in terms of the result. A 1000-frame video sequence sample with sparse, smoothly moving targets, reconstructed from a 125 s DIDSON recording, was analysed under two distinct scenarios, and an elementary detection method was used to assess and compare the resulting foreground (target) masks. The results indicate a high sensitivity to motion, as well as to the visual characteristics of targets, with the resulting foreground masks generally capturing fish targets on the majority of frames, potentially with small gaps of undetected targets, lasting for no more than a few frames. Despite the high computational overhead, implementation refinements could increase computational feasibility, while an extension of the algorithms, in order to include the steps of target detection and tracking, could further improve automation and potentially provide an efficient tool for the automated preliminary assessment of voluminous DIDSON data recordings. Full article
(This article belongs to the Special Issue Water Resources Management: Advances in Machine Learning Approaches)
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19 pages, 2417 KiB  
Article
Benefit Evaluation of Water and Soil Conservation Measures in Shendong Based on Particle Swarm Optimization and the Analytic Hierarchy Process
by Yangnan Guo, Guoqing Chen, Rigan Mo, Meng Wang and Yuying Bao
Water 2020, 12(7), 1955; https://doi.org/10.3390/w12071955 - 09 Jul 2020
Cited by 15 | Viewed by 3154
Abstract
Soil erosion is the main threat to the stability of ecological environment and the harmonious development of society in Shendong Mining Area. The main causes of this threat include the strong interference of natural characteristics and land development. Scientific soil and water conservation [...] Read more.
Soil erosion is the main threat to the stability of ecological environment and the harmonious development of society in Shendong Mining Area. The main causes of this threat include the strong interference of natural characteristics and land development. Scientific soil and water conservation measures can coordinate the contradictions among coal economic development, ecological protection, and residents’ prosperity. Based on particle swarm optimization and analytic hierarchy process, the benefit evaluation system of soil and water conservation measures in Shendong Mining Area is established. The weight ratio of three kinds of benefits in Shendong coal mine collapse area is: ecological benefit > social benefit > economic benefit. The conclusion shows that the implementation of the national policy and the effect of mining area management meet the expectation. Therefore, this study provides effective reference and reasonable suggestions for soil and water conservation in Shendong Mining Area. In terms of control measures, bioengineering measures, such as increased coverage of forest and grass as well as reasonable transformation of the landscape pattern of micro landform, can improve the degree of soil erosion control, optimize the land use structure, and improve the land use rate. Full article
(This article belongs to the Special Issue Water Resources Management: Advances in Machine Learning Approaches)
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20 pages, 1297 KiB  
Article
Risk-Informed Prediction of Dredging Project Duration Using Stochastic Machine Learning
by Jui-Sheng Chou and Ji-Wei Lin
Water 2020, 12(6), 1643; https://doi.org/10.3390/w12061643 - 08 Jun 2020
Cited by 4 | Viewed by 1982
Abstract
Dredging engineering projects are complex because they involve greater uncertainty from the natural environment, social needs, government policy and many stakeholders. Engineering companies submit tenders that draw on similar cases undertaken in recent years. However, weather, earthquakes, typhoons and other disasters often change [...] Read more.
Dredging engineering projects are complex because they involve greater uncertainty from the natural environment, social needs, government policy and many stakeholders. Engineering companies submit tenders that draw on similar cases undertaken in recent years. However, weather, earthquakes, typhoons and other disasters often change landforms. Therefore, evaluating the duration of dredging projects with reference to only a few previous cases is inadequate, often leading to an unnecessarily long construction duration if the scope of the project is not clearly defined at the early phase. The goal of this investigation aimed to estimate project duration at the beginning of construction and the probability of risk. Evolutionary machine learning was used to build a deterministic model of dredging project duration. Monte Carlo simulation was then utilized to establish the probabilistic distribution of the project duration based on historical patterns. The analytical outputs are displayed through a graphical user interface that provides project coordinators with a means of assessing the uncertainty of project duration in the initial phase of the project. This study will provide a practical reference for contractors and the Water Resources Agency. Full article
(This article belongs to the Special Issue Water Resources Management: Advances in Machine Learning Approaches)
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23 pages, 1430 KiB  
Article
Analysis and Prediction of Dammed Water Level in a Hydropower Reservoir Using Machine Learning and Persistence-Based Techniques
by C. Castillo-Botón, D. Casillas-Pérez, C. Casanova-Mateo, L. M. Moreno-Saavedra, B. Morales-Díaz, J. Sanz-Justo, P. A. Gutiérrez and S. Salcedo-Sanz
Water 2020, 12(6), 1528; https://doi.org/10.3390/w12061528 - 27 May 2020
Cited by 26 | Viewed by 4330
Abstract
This paper presents long- and short-term analyses and predictions of dammed water level in a hydropower reservoir. The long-term analysis was carried out by using techniques such as detrended fluctuation analysis, auto-regressive models, and persistence-based algorithms. On the other hand, the short-term analysis [...] Read more.
This paper presents long- and short-term analyses and predictions of dammed water level in a hydropower reservoir. The long-term analysis was carried out by using techniques such as detrended fluctuation analysis, auto-regressive models, and persistence-based algorithms. On the other hand, the short-term analysis of the dammed water level in the hydropower reservoir was modeled as a prediction problem, where machine learning regression techniques were studied. A set of models, including different types of neural networks, Support Vector regression, or Gaussian processes was tested. Real data from a hydropower reservoir located in Galicia, Spain, qwew considered, together with predictive variables from upstream measuring stations. We show that the techniques presented in this paper offer an excellent tool for the long- and short-term analysis and prediction of dammed water level in reservoirs for hydropower purposes, especially important for the management of water resources in areas with hydrology stress, such as Spain. Full article
(This article belongs to the Special Issue Water Resources Management: Advances in Machine Learning Approaches)
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12 pages, 2369 KiB  
Article
Comparing Activation Functions in Modeling Shoreline Variation Using Multilayer Perceptron Neural Network
by Je-Chian Chen and Yu-Min Wang
Water 2020, 12(5), 1281; https://doi.org/10.3390/w12051281 - 30 Apr 2020
Cited by 26 | Viewed by 3019
Abstract
The study has modeled shoreline changes by using a multilayer perceptron (MLP) neural network with the data collected from five beaches in southern Taiwan. The data included aerial survey maps of the Forestry Bureau for years 1982, 2002, and 2006, which served as [...] Read more.
The study has modeled shoreline changes by using a multilayer perceptron (MLP) neural network with the data collected from five beaches in southern Taiwan. The data included aerial survey maps of the Forestry Bureau for years 1982, 2002, and 2006, which served as predictors, while the unmanned aerial vehicle (UAV) surveyed data of 2019 served as the respondent. The MLP was configured using five different activation functions with the aim of evaluating their significance. These functions were Identity, Tahn, Logistic, Exponential, and Sine Functions. The results have shown that the performance of an MLP model may be affected by the choice of an activation function. Logistic and the Tahn activation functions outperformed the other models, with Logistic performing best in three beaches and Tahn having the rest. These findings suggest that the application of machine learning to shoreline changes should be accompanied by an extensive evaluation of the different activation functions. Full article
(This article belongs to the Special Issue Water Resources Management: Advances in Machine Learning Approaches)
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19 pages, 4329 KiB  
Article
Machine Learning Approaches for Predicting Health Risk of Cyanobacterial Blooms in Northern European Lakes
by Nikolaos Mellios, S. Jannicke Moe and Chrysi Laspidou
Water 2020, 12(4), 1191; https://doi.org/10.3390/w12041191 - 22 Apr 2020
Cited by 19 | Viewed by 5447
Abstract
Cyanobacterial blooms are considered a major threat to global water security with documented impacts on lake ecosystems and public health. Given that cyanobacteria possess highly adaptive traits that favor them to prevail under different and often complicated stressor regimes, predicting their abundance is [...] Read more.
Cyanobacterial blooms are considered a major threat to global water security with documented impacts on lake ecosystems and public health. Given that cyanobacteria possess highly adaptive traits that favor them to prevail under different and often complicated stressor regimes, predicting their abundance is challenging. A dataset from 822 Northern European lakes is used to determine which variables better explain the variation of cyanobacteria biomass (CBB) by means of stepwise multiple linear regression. Chlorophyll-a (Chl-a) and total nitrogen (TN) provided the best modelling structure for the entire dataset, while for subsets of shallow and deep lakes, Chl-a, mean depth, TN and TN/TP explained part of the variance in CBB. Path analysis was performed and corroborated these findings. Finally, CBB was translated to a categorical variable according to risk levels for human health associated with the use of lakes for recreational activities. Several machine learning methods, namely Decision Tree, K-Nearest Neighbors, Support-vector Machine and Random Forest, were applied showing a remarkable ability to predict the risk, while Random Forest parameters were tuned and optimized, achieving a 95.81% accuracy, exceeding the performance of all other machine learning methods tested. A confusion matrix analysis is performed for all machine learning methods, identifying the potential of each method to correctly predict CBB risk levels and assessing the extent of false alarms; random forest clearly outperforms the other methods with very promising results. Full article
(This article belongs to the Special Issue Water Resources Management: Advances in Machine Learning Approaches)
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