TY - JOUR
T1 - Groundwater level prediction using machine learning models
T2 - A comprehensive review
AU - Tao, Hai
AU - Hameed, Mohammed Majeed
AU - Marhoon, Haydar Abdulameer
AU - Zounemat-Kermani, Mohammad
AU - Heddam, Salim
AU - Sungwon, Kim
AU - Sulaiman, Sadeq Oleiwi
AU - Tan, Mou Leong
AU - Sa'adi, Zulfaqar
AU - Mehr, Ali Danandeh
AU - Allawi, Mohammed Falah
AU - Abba, S. I.
AU - Zain, Jasni Mohamad
AU - Falah, Mayadah W.
AU - Jamei, Mehdi
AU - Bokde, Neeraj Dhanraj
AU - Bayatvarkeshi, Maryam
AU - Al-Mukhtar, Mustafa
AU - Bhagat, Suraj Kumar
AU - Tiyasha, Tiyasha
AU - Khedher, Khaled Mohamed
AU - Al-Ansari, Nadhir
AU - Shahid, Shamsuddin
AU - Yaseen, Zaher Mundher
N1 - Publisher Copyright:
© 2022 The Authors
PY - 2022/6/7
Y1 - 2022/6/7
N2 - Developing accurate soft computing methods for groundwater level (GWL) forecasting is essential for enhancing the planning and management of water resources. Over the past two decades, significant progress has been made in GWL prediction using machine learning (ML) models. Several review articles have been published, reporting the advances in this field up to 2018. However, the existing review articles do not cover several aspects of GWL simulations using ML, which are significant for scientists and practitioners working in hydrology and water resource management. The current review article aims to provide a clear understanding of the state-of-the-art ML models implemented for GWL modeling and the milestones achieved in this domain. The review includes all of the types of ML models employed for GWL modeling from 2008 to 2020 (138 articles) and summarizes the details of the reviewed papers, including the types of models, data span, time scale, input and output parameters, performance criteria used, and the best models identified. Furthermore, recommendations for possible future research directions to improve the accuracy of GWL prediction models and enhance the related knowledge are outlined.
AB - Developing accurate soft computing methods for groundwater level (GWL) forecasting is essential for enhancing the planning and management of water resources. Over the past two decades, significant progress has been made in GWL prediction using machine learning (ML) models. Several review articles have been published, reporting the advances in this field up to 2018. However, the existing review articles do not cover several aspects of GWL simulations using ML, which are significant for scientists and practitioners working in hydrology and water resource management. The current review article aims to provide a clear understanding of the state-of-the-art ML models implemented for GWL modeling and the milestones achieved in this domain. The review includes all of the types of ML models employed for GWL modeling from 2008 to 2020 (138 articles) and summarizes the details of the reviewed papers, including the types of models, data span, time scale, input and output parameters, performance criteria used, and the best models identified. Furthermore, recommendations for possible future research directions to improve the accuracy of GWL prediction models and enhance the related knowledge are outlined.
KW - Catchment sustainability
KW - Groundwater level
KW - Input parameters
KW - Machine learning
KW - Prediction performance
KW - State-of-the-art
UR - http://www.scopus.com/inward/record.url?scp=85126854743&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2022.03.014
DO - 10.1016/j.neucom.2022.03.014
M3 - Short survey
AN - SCOPUS:85126854743
SN - 0925-2312
VL - 489
SP - 271
EP - 308
JO - Neurocomputing
JF - Neurocomputing
ER -
