Groundwater level forecasting with machine learning models: A review
Groundwater, the world's most abundant source of freshwater, is rapidly depleting in many regions due to a variety of factors. Accurate forecasting of groundwater level (GWL) is essential for effective management of this vital resource, but it remains a complex and challenging task. In recent y...
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my.uniten.dspace-367202025-03-03T15:44:10Z Groundwater level forecasting with machine learning models: A review Boo K.B.W. El-Shafie A. Othman F. Khan M.M.H. Birima A.H. Ahmed A.N. 58742096300 16068189400 36630785100 16304362800 23466519000 57214837520 Environmental Monitoring Forecasting Groundwater Machine Learning Neural Networks, Computer ground water artificial neural network environmental monitoring forecasting machine learning procedures Groundwater, the world's most abundant source of freshwater, is rapidly depleting in many regions due to a variety of factors. Accurate forecasting of groundwater level (GWL) is essential for effective management of this vital resource, but it remains a complex and challenging task. In recent years, there has been a notable increase in the use of machine learning (ML) techniques to model GWL, with many studies reporting exceptional results. In this paper, we present a comprehensive review of 142 relevant articles indexed by the Web of Science from 2017 to 2023, focusing on key ML models, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), evolutionary computing (EC), deep learning (DL), ensemble learning (EN), and hybrid-modeling (HM). We also discussed key modeling concepts such as dataset size, data splitting, input variable selection, forecasting time-step, performance metrics (PM), study zones, and aquifers, highlighting best practices for optimal GWL forecasting with ML. This review provides valuable insights and recommendations for researchers and water management agencies working in the field of groundwater management and hydrology. ? 2024 Elsevier Ltd Final 2025-03-03T07:44:09Z 2025-03-03T07:44:09Z 2024 Review 10.1016/j.watres.2024.121249 2-s2.0-85184785090 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184785090&doi=10.1016%2fj.watres.2024.121249&partnerID=40&md5=8a440ed7daaf00792ea5e0ed3a32f9e3 https://irepository.uniten.edu.my/handle/123456789/36720 252 121249 Elsevier Ltd Scopus |
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Environmental Monitoring Forecasting Groundwater Machine Learning Neural Networks, Computer ground water artificial neural network environmental monitoring forecasting machine learning procedures Boo K.B.W. El-Shafie A. Othman F. Khan M.M.H. Birima A.H. Ahmed A.N. Groundwater level forecasting with machine learning models: A review |
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Groundwater, the world's most abundant source of freshwater, is rapidly depleting in many regions due to a variety of factors. Accurate forecasting of groundwater level (GWL) is essential for effective management of this vital resource, but it remains a complex and challenging task. In recent years, there has been a notable increase in the use of machine learning (ML) techniques to model GWL, with many studies reporting exceptional results. In this paper, we present a comprehensive review of 142 relevant articles indexed by the Web of Science from 2017 to 2023, focusing on key ML models, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), evolutionary computing (EC), deep learning (DL), ensemble learning (EN), and hybrid-modeling (HM). We also discussed key modeling concepts such as dataset size, data splitting, input variable selection, forecasting time-step, performance metrics (PM), study zones, and aquifers, highlighting best practices for optimal GWL forecasting with ML. This review provides valuable insights and recommendations for researchers and water management agencies working in the field of groundwater management and hydrology. ? 2024 Elsevier Ltd |
| author2 |
58742096300 |
| author_facet |
58742096300 Boo K.B.W. El-Shafie A. Othman F. Khan M.M.H. Birima A.H. Ahmed A.N. |
| format |
Review |
| author |
Boo K.B.W. El-Shafie A. Othman F. Khan M.M.H. Birima A.H. Ahmed A.N. |
| author_sort |
Boo K.B.W. |
| title |
Groundwater level forecasting with machine learning models: A review |
| title_short |
Groundwater level forecasting with machine learning models: A review |
| title_full |
Groundwater level forecasting with machine learning models: A review |
| title_fullStr |
Groundwater level forecasting with machine learning models: A review |
| title_full_unstemmed |
Groundwater level forecasting with machine learning models: A review |
| title_sort |
groundwater level forecasting with machine learning models: a review |
| publisher |
Elsevier Ltd |
| publishDate |
2025 |
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1825816030229823488 |
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13.244413 |