Evaluation and Prediction of Groundwater Quality for Irrigation Using an Integrated Water Quality Indices, Machine Learning Models and GIS Approaches: A Representative Case Study

Water, Journal Year: 2023, Volume and Issue: 15(4), P. 694 - 694

Published: Feb. 10, 2023

Agriculture has significantly aided in meeting the food needs of growing population. In addition, it boosted economic development irrigated regions. this study, an assessment groundwater (GW) quality for agricultural land was carried out El Kharga Oasis, Western Desert Egypt. Several irrigation water indices (IWQIs) and geographic information systems (GIS) were used modeling development. Two machine learning (ML) models (i.e., adaptive neuro-fuzzy inference system (ANFIS) support vector (SVM)) developed prediction eight IWQIs, including index (IWQI), sodium adsorption ratio (SAR), soluble percentage (SSP), potential salinity (PS), residual carbonate (RSC), Kelley (KI). The physicochemical parameters included T°, pH, EC, TDS, K+, Na+, Mg2+, Ca2+, Cl−, SO42−, HCO3−, CO32−, NO3−, they measured 140 GW wells. hydrochemical facies resources Ca-Mg-SO4, mixed Ca-Mg-Cl-SO4, Na-Cl, Ca-Mg-HCO3, Na-Ca-HCO3 types, which revealed silicate weathering, dissolution gypsum/calcite/dolomite/ halite, rock–water interactions, reverse ion exchange processes. IWQI, SAR, KI, PS showed that majority samples categorized purposes into no restriction (67.85%), excellent (100%), good (57.85%), to (65.71%), respectively. Moreover, selected as safe according SSP RSC. performance simulation evaluated based on several skills criteria, ANFIS model SVM capable simulating IWQIs with reasonable accuracy both training “determination coefficient (R2)” (R2 = 0.99 0.97) testing 0.97 0.76). presented models’ promising illustrates their use IWQI prediction. findings indicate ML methods geographically dispersed hydrogeochemical data, such SVM, be assessing irrigation. proposed methodological approach offers a useful tool identifying crucial components evolution mitigation measures related management arid semi-arid environments.

Language: Английский

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Application of Machine Learning in Water Resources Management: A Systematic Literature Review

Water, Journal Year: 2023, Volume and Issue: 15(4), P. 620 - 620

Published: Feb. 5, 2023

In accordance with the rapid proliferation of machine learning (ML) and data management, ML applications have evolved to encompass all engineering disciplines. Owing importance world’s water supply throughout rest this century, much research has been concentrated on application strategies integrated resources management (WRM). Thus, a thorough well-organized review that is required. To accommodate underlying knowledge interests both artificial intelligence (AI) unresolved issues in WRM, overview divides core fundamentals, major applications, ongoing into two sections. First, basic are categorized three main groups, prediction, clustering, reinforcement learning. Moreover, literature organized each field according new perspectives, patterns indicated so attention can be directed toward where headed. second part, less investigated WRM addressed provide grounds for future studies. The widespread tools projected accelerate formation sustainable plans over next decade.

Language: Английский

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Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 129, P. 107559 - 107559

Published: Dec. 3, 2023

Language: Английский

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Predicting lake water quality index with sensitivity-uncertainty analysis using deep learning algorithms

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 406, P. 136885 - 136885

Published: April 3, 2023

Language: Английский

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A Comprehensive Review of Conventional, Machine Leaning, and Deep Learning Models for Groundwater Level (GWL) Forecasting

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(4), P. 2743 - 2743

Published: Feb. 20, 2023

Groundwater level (GWL) refers to the depth of water table or below Earth’s surface in underground formations. It is an important factor managing and sustaining groundwater resources that are used for drinking water, irrigation, other purposes. prediction a critical aspect resource management requires accurate efficient modelling techniques. This study reviews most commonly conventional numerical, machine learning, deep learning models predicting GWL. Significant advancements have been made terms efficiency over last two decades. However, while researchers primarily focused on monthly, weekly, daily, hourly GWL, managers strategists require multi-year GWL simulations take effective steps towards ensuring sustainable supply groundwater. In this paper, we consider collection state-of-the-art theories develop design novel methodology improve field evaluation. We examined 109 research articles published from 2008 2022 investigated different Finally, concluded approaches Moreover, provide possible future directions recommendations enhance accuracy relevant understanding.

Language: Английский

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Assessment of groundwater suitability for sustainable irrigation: A comprehensive study using indexical, statistical, and machine learning approaches

Groundwater for Sustainable Development, Journal Year: 2023, Volume and Issue: 24, P. 101059 - 101059

Published: Dec. 13, 2023

Language: Английский

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Prediction of weighted arithmetic water quality index for urban water quality using ensemble machine learning model

Chemosphere, Journal Year: 2024, Volume and Issue: 352, P. 141393 - 141393

Published: Feb. 5, 2024

Language: Английский

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Conceptualizing future groundwater models through a ternary framework of multisource data, human expertise, and machine intelligence

Water Research, Journal Year: 2024, Volume and Issue: 257, P. 121679 - 121679

Published: April 26, 2024

Language: Английский

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Groundwater level forecasting with machine learning models: A review

Water Research, Journal Year: 2024, Volume and Issue: 252, P. 121249 - 121249

Published: Feb. 2, 2024

Language: Английский

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Predicting groundwater level using traditional and deep machine learning algorithms

Frontiers in Environmental Science, Journal Year: 2024, Volume and Issue: 12

Published: Feb. 16, 2024

This research aims to evaluate various traditional or deep machine learning algorithms for the prediction of groundwater level (GWL) using three key input variables specific Izeh City in Khuzestan province Iran: extraction rate (E), rainfall (R), and river flow (P) (with 3 km distance). Various (DML) algorithms, including convolutional neural network (CNN), recurrent (RNN), support vector (SVM), decision tree (DT), random forest (RF), generative adversarial (GAN), were evaluated. The (CNN) algorithm demonstrated superior performance among all evaluated this study. CNN model exhibited robustness against noise variability, scalability handling large datasets with multiple variables, parallelization capabilities fast processing. Moreover, it autonomously learned identified data patterns, resulting fewer outlier predictions. achieved highest accuracy GWL prediction, an RMSE 0.0558 R 2 0.9948. It also showed no predictions, indicating its reliability. Spearman Pearson correlation analyses revealed that P E dataset’s most influential on GWL. has significant implications water resource management Iran, aiding conservation efforts increasing local crop productivity. approach can be applied predicting global regions facing scarcity due population growth. Future researchers are encouraged consider these factors more accurate Additionally, algorithm’s further enhanced by incorporating additional variables.

Language: Английский

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