Groundwater level prediction using machine learning models: a comprehensive review

• Main Authors: Tao, Hai, Hameed, Mohammed Majeed, Marhoon, Haydar Abdulameer, Mohammad Zounemat Kermani, Mohammad Zounemat Kermani, Heddam, Salim, Kim, Sungwon, Sulaiman, Sadeq Oleiwi, Tan, Mou Leong, Sa’adi, Zulfaqar, Mehr, Ali Danandeh, Allawi, Mohammed Falah, Abba, S. I., Mohamad Zain, Jasni, W. Falah, Mayadah, Jamei, Mehdi, Bokde, Neeraj Dhanraj, Bayatvarkeshi, Maryam, Al-Mukhtar, Mustafa, Bhagat, Suraj Kumar, Tiyasha, Tiyasha, Khedher, Khaled Mohamed, Al-Ansari, Nadhir, Shahid, Shamsuddin, Yaseen, Zaher Mundher
• Format: Article
• Language: English
• Published: Elsevier B.V. 2022
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://eprints.utm.my/103432/1/ShamsuddinShahid2022_GroundwaterLevelPredictionusingMachineLearning.pdf; http://eprints.utm.my/103432/; http://dx.doi.org/10.1016/j.neucom.2022.03.014
• Institution: Universiti Teknologi Malaysia

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 ha...