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Applications of Machine Learning Models to Analyze Water Management Problems

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Water Management".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 14390

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Special Issue Editors

Dr. Mohammad Najafzadeh

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Guest Editor

Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, Kerman P.O.Box 7631885356, Iran
Interests: energy efficiency of waves; scouring; currents; rip current; marine structures
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Francesco Granata

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Guest Editor

Department of Civil and Mechanical Engineering (DICEM), University of Cassino and Southern Lazio, Via Di Biasio 43, 03043 Cassino, FR, Italy
Interests: hydrology; environmental engineering; civil engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the wide range of breakthroughs in machine learning techniques, enormous improvements in water management problems have recently been experienced to significantly make environment sustainable. Over the past decades, water resources-related problems have always become the cornerstone of the issues that researchers have made attempts to enhance precision level of water management problems such as monitoring rivers pollutants, groundwater quality assessment, optimization of water resources systems, flood monitoring, drought, and evapotranspiration prediction. Nowadays, there is a ferocious demand for usability of newly-advanced machine-learning techniques for driving physical behaviors of various natural hazards that have been frequently occurred in environment and consequently have detrimental impacts on sustainability of water resources management. In the present special issue, researchers are respectfully invited to submit their research works on the use of machine learning models to address significant issues in water resources management problems. We are fond of receiving research works focusing on novel usability of machine learning models for improving the water management problems in comparison with traditional techniques. Additionally, the research works on the experimental study of water management issues accompanying with machine learning modeling is highly welcoming. The ultimate findings of submitted research works should put on two ways: enhancing knowledge extraction of important environmental events in water resources management and providing a contemporary insight on the implementation of machine learning models for the above-mentioned issues.

The present Special Issue will generally provide a platform for presenting precious experience on the recent achievements of water resources management. In addition to this, all environmental aspects of water resources management related to the sustainable development are privileged.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

Surface water quality and groundwater modeling by machine learning models;
Implementation of machine learning models for modelling precipitation, evapotranspiration, flow, and drought;
Flood risk monitoring by newly-developed machine learning techniques;
Risk, Reliability, and uncertainty analysis of various problems in water resources management;
Machine Learning techniques applications into design of storm sewers;
Applications of remote sensing for all aspects of monitoring water resources.

We look forward to receiving your contributions.

Prof. Dr. Mohammad Najafzadeh
Prof. Dr. Francesco Granata
Guest Editors

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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

water resources pollution
remote sensing
soft computing models
water resources systems analysis
evapotranspiration
intelligent design of storm sewer
flood risk
reliability analysis
drought
scouring
natural hazards

Published Papers (6 papers)

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Research

18 pages, 2812 KiB

Open AccessArticle

Receiving Robust Analysis of Spatial and Temporary Variation of Agricultural Water Use Efficiency While Considering Environmental Factors: On the Evaluation of Data Envelopment Analysis Technique

by Hongguang Dong, Jie Geng and Yue Xu

Sustainability 2023, 15(5), 3926; https://doi.org/10.3390/su15053926 - 21 Feb 2023

Cited by 1 | Viewed by 988

Abstract

With accelerated urbanisation, continued growth in water demand and the external pressure of water demand from the South–North Water Transfer Project, agricultural water use in Jiangsu is facing a critical situation. Therefore, it is important to explore the spatial and temporal variation in agricultural water use efficiency in order to clarify the pathway for improving agricultural water use efficiency. Firstly, the Super-Slacks-Based Measure (SBM) model was utilized to measure agricultural water use efficiency in Jiangsu Province, China, from 2011 to 2020, and secondly, a fixed-effects model was used to investigate agricultural water use efficiency and the factors influencing it in 13 prefectures in Jiangsu Province in both time and space. The results show that (1) the overall value of agricultural water use efficiency in Jiangsu Province is below 1, which means that agricultural water use efficiency in Jiangsu Province is low and far from the effective boundary, and there is more room for improvement in agricultural water use efficiency; (2) a total of 92% of prefectures in Jiangsu Province have input redundancy, which seriously inhibits the progress of agricultural water use efficiency in Jiangsu Province, among which the redundancy of total agricultural machinery power and agricultural water use is the highest; (3) Regarding total factor productivity and its decomposition index for agricultural use in Jiangsu Province, in the time dimension, the number of professional and technical personnel inputs has a positive impact on agricultural water use efficiency. In the spatial dimension, the number of professional and technical personnel inputs, industrial structure and arable land area have a positive impact on improving regional agricultural water use efficiency, among which the industrial structure has a smaller contribution to agricultural water use efficiency. Full article

(This article belongs to the Special Issue Applications of Machine Learning Models to Analyze Water Management Problems)

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19 pages, 4414 KiB

Open AccessArticle

Effects of Surge Tank Geometry on the Water Hammer Phenomenon: Numerical Investigation

by Mohammad Mahmoudi-Rad and Mohammad Najafzadeh

Sustainability 2023, 15(3), 2312; https://doi.org/10.3390/su15032312 - 27 Jan 2023

Cited by 3 | Viewed by 2210

Abstract

A surge tank, as one of the most common control facilities, is applied to control head pressure levels in long pressurized pipelines during the water hammer occurrence. The cost-effective operation of surge tanks is highly affected by their characteristics (i.e., surge tank diameter and inlet diameter of surge tanks) and can effectively reduce the repercussion of water hammers. This investigation utilized the method of characteristics (MOC) in order to simulate the behavior of transient flow at the surge tank upstream and the head pressure fluctuations regime for the hydraulic system of a hydropower dam. Firstly, the MOC model was validated by experimental observations. The various types of boundary conditions (i.e., sure tank, reservoir, branch connection of three pipes, series pipes, and downstream valve) were applied to investigate the simultaneous effects of the surge tank properties. In this way, all the simulations of water hammer equations were conducted for nine various combinations of surge tank diameter (D) and inlet diameter of surge tank (d). The results of this study indicated that for the surge tank design with D = 6 m and d = 3.4 m, head pressure fluctuations reached the minimum level in the large section of the pipeline which is the surge tank upstream. Additionally, the occurrence of the water hammer phenomenon was probable in the initial section of the pipeline. Full article

(This article belongs to the Special Issue Applications of Machine Learning Models to Analyze Water Management Problems)

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21 pages, 3413 KiB

Open AccessArticle

Predicting Cu(II) Adsorption from Aqueous Solutions onto Nano Zero-Valent Aluminum (nZVAl) by Machine Learning and Artificial Intelligence Techniques

by Ahmed H. Sadek, Omar M. Fahmy, Mahmoud Nasr and Mohamed K. Mostafa

Sustainability 2023, 15(3), 2081; https://doi.org/10.3390/su15032081 - 21 Jan 2023

Cited by 18 | Viewed by 2426

Abstract

Predicting the heavy metals adsorption performance from contaminated water is a major environment-associated topic, demanding information on different machine learning and artificial intelligence techniques. In this research, nano zero-valent aluminum (nZVAl) was tested to eliminate Cu(II) ions from aqueous solutions, modeling and predicting the Cu(II) removal efficiency (R%) using the adsorption factors. The prepared nZVAl was characterized for elemental composition and surface morphology and texture. It was depicted that, at an initial Cu(II) level (C_o) 50 mg/L, nZVAl dose 1.0 g/L, pH 5, mixing speed 150 rpm, and 30 °C, the R% was 53.2 ± 2.4% within 10 min. The adsorption data were well defined by the Langmuir isotherm model (R²: 0.925) and pseudo-second-order (PSO) kinetic model (R²: 0.9957). The best modeling technique used to predict R% was artificial neural network (ANN), followed by support vector regression (SVR) and linear regression (LR). The high accuracy of ANN, with MSE < 10⁻⁵, suggested its applicability to maximize the nZVAl performance for removing Cu(II) from contaminated water at large scale and under different operational conditions. Full article

(This article belongs to the Special Issue Applications of Machine Learning Models to Analyze Water Management Problems)

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15 pages, 4470 KiB

Open AccessArticle

Spatial and Temporal Evolution of Vegetation Water Consumption in Arid and Semi-Arid Areas against the Background of Returning Farmland to Forestland

by Ting Guo, Quanhua Hou, Yan Wu and Lingda Zhang

Sustainability 2022, 14(22), 14959; https://doi.org/10.3390/su142214959 - 11 Nov 2022

Cited by 3 | Viewed by 1105

Abstract

Sustainable development in arid and semi-arid areas is largely constrained by water resources. Expanding ecological space is considered an effective way to conserve water resources. The innovation of this study is the analysis of water consumption in different land-use types from a complete watershed scale, which can evaluate space management against the background of returning farmland to forestland during the past 20 years, and provide suggestions for future space management in semi-arid areas. Based on meteorological data and GIS technology, the current study quantitatively analyzes the spatial and temporal variation characteristics of the water consumption of different vegetation growth stages in the Yanhe River Basin by using the improved Penman formula. The results show that the water consumption of vegetation in the Yanhe River Basin increased from 0.44 km³ in 2000 to 0.68 km³ in 2020. The water consumption of vegetation showed obvious spatial heterogeneity, with the highest value in the central Baota area (1.094 km³) followed by the western Ansai region (0.727 km³), whereas the consumption in the eastern Yanchang area is relatively low (0.483 km³). In addition, the annual average water consumption is (0.381 km³). The cultivated land consumes the most water (0.21 km³), while the woodland consumes the least (0.072 km³). The water consumption per unit area of forested land is the highest, reaching 190 m, and the water consumption of low-coverage grassland is the lowest, only reaching 50 m. Vegetation distribution change could be the main influencing factor of vegetation water consumption change in the Yanhe River Basin. Through the establishment of the sustainable development path of ecological space with water as the core, the high-quality development of ecological environments in arid and semi-arid areas will be achieved. Full article

(This article belongs to the Special Issue Applications of Machine Learning Models to Analyze Water Management Problems)

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28 pages, 4488 KiB

Open AccessArticle

Lake Level Evolution of the Largest Freshwater Lake on the Mediterranean Islands through Drought Analysis and Machine Learning

by Željka Brkić and Mladen Kuhta

Sustainability 2022, 14(16), 10447; https://doi.org/10.3390/su141610447 - 22 Aug 2022

Cited by 3 | Viewed by 1583

Abstract

Vrana Lake on the karst island of Cres (Croatia) is the largest freshwater lake in the Mediterranean islands. The lake cryptodepression, filled with 220 million m³ of fresh drinking water, represents a specific karst phenomenon. To better understand the impact of water level change drivers, the occurrence of meteorological and hydrological droughts was analysed. Basic machine learning methods (ML) such as the multiple linear regression (MLR), multiple nonlinear regression (MNLR), and artificial neural network (ANN) were used to simulate water levels. Modelling was carried out considering annual inputs of precipitation, air temperature, and abstraction rate as well as their influential lags which were determined by auto-correlation and cross-correlation techniques. Hydrological droughts have been recorded since 1986, and after 2006 a series of mostly mild hot to moderate hot years was recorded. All three ML models have been trained to recognize extreme conditions in the form of less precipitation, high abstraction rate, and, consequently, low water levels in the testing (predicting) period. The best statistical indicators were achieved with the MNLR model. The methodologies applied in the study were found to be useful tools for the analysis of changes in water levels. Extended monitoring of water balance elements should precede any future increase in the abstraction rate. Full article

(This article belongs to the Special Issue Applications of Machine Learning Models to Analyze Water Management Problems)

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21 pages, 3704 KiB

Open AccessArticle

Precipitation Forecasting in Northern Bangladesh Using a Hybrid Machine Learning Model

by Fabio Di Nunno, Francesco Granata, Quoc Bao Pham and Giovanni de Marinis

Sustainability 2022, 14(5), 2663; https://doi.org/10.3390/su14052663 - 24 Feb 2022

Cited by 13 | Viewed by 2699

Abstract

Precipitation forecasting is essential for the assessment of several hydrological processes. This study shows that based on a machine learning approach, reliable models for precipitation prediction can be developed. The tropical monsoon-climate northern region of Bangladesh, including the Rangpur and Sylhet division, was chosen as the case study. Two machine learning algorithms were used: M5P and support vector regression. Moreover, a novel hybrid model based on the two algorithms was developed. The performance of prediction models was assessed by means of evaluation metrics and graphical representations. A sensitivity analysis was also carried out to assess the prediction accuracy as the number of exogenous inputs reduces and lag times increases. Overall, the hybrid model M5P-SVR led to the best predictions among used models in this study, with R² values up to 0.87 and 0.92 for the stations of Rangpur and Sylhet, respectively. Full article

(This article belongs to the Special Issue Applications of Machine Learning Models to Analyze Water Management Problems)

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