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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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Main Authors:	Boo, Kenneth Beng Wee, El-Shafie, Ahmed, Othman, Faridah, Khan, Md. Munir Hayet, Birima, Ahmed H., Ahmed, Ali Najah
Format:	Article
Published:	Elsevier 2024
Subjects:	TA Engineering (General). Civil engineering (General) TD Environmental technology. Sanitary engineering
Online Access:	http://eprints.um.edu.my/45651/ https://doi.org/10.1016/j.watres.2024.121249
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id	my.um.eprints.45651
record_format	eprints
spelling	my.um.eprints.456512024-11-07T06:17:45Z http://eprints.um.edu.my/45651/ Groundwater level forecasting with machine learning models: A review Boo, Kenneth Beng Wee El-Shafie, Ahmed Othman, Faridah Khan, Md. Munir Hayet Birima, Ahmed H. Ahmed, Ali Najah TA Engineering (General). Civil engineering (General) TD Environmental technology. Sanitary engineering 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. Elsevier 2024-03 Article PeerReviewed Boo, Kenneth Beng Wee and El-Shafie, Ahmed and Othman, Faridah and Khan, Md. Munir Hayet and Birima, Ahmed H. and Ahmed, Ali Najah (2024) Groundwater level forecasting with machine learning models: A review. Water Research, 252. p. 121249. ISSN 0043-1354, DOI https://doi.org/10.1016/j.watres.2024.121249 <https://doi.org/10.1016/j.watres.2024.121249>. https://doi.org/10.1016/j.watres.2024.121249 10.1016/j.watres.2024.121249
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	TA Engineering (General). Civil engineering (General) TD Environmental technology. Sanitary engineering
spellingShingle	TA Engineering (General). Civil engineering (General) TD Environmental technology. Sanitary engineering Boo, Kenneth Beng Wee El-Shafie, Ahmed Othman, Faridah Khan, Md. Munir Hayet Birima, Ahmed H. Ahmed, Ali Najah Groundwater level forecasting with machine learning models: A review
description	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.
format	Article
author	Boo, Kenneth Beng Wee El-Shafie, Ahmed Othman, Faridah Khan, Md. Munir Hayet Birima, Ahmed H. Ahmed, Ali Najah
author_facet	Boo, Kenneth Beng Wee El-Shafie, Ahmed Othman, Faridah Khan, Md. Munir Hayet Birima, Ahmed H. Ahmed, Ali Najah
author_sort	Boo, Kenneth Beng Wee
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
publishDate	2024
url	http://eprints.um.edu.my/45651/ https://doi.org/10.1016/j.watres.2024.121249
_version_	1816130433045233664
score	13.222552

Groundwater level forecasting with machine learning models: A review

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