作者
Hai Tao, Mohammed Majeed Hameed, Haydar Abdulameer Marhoon, Mohammad Zounemat-Kermani, Salim Heddam, Sungwon Kim, Sadeq Oleiwi Sulaiman, Mou Leong Tan, Zulfaqar Sa’adi, Ali Danandeh Mehr, Mohammed Falah Allawi, Sani Isah Abba, Jasni Mohamad Zain, Mayadah W Falah, Mehdi Jamei, Neeraj Dhanraj Bokde, Maryam Bayatvarkeshi, Mustafa Al-Mukhtar, Suraj Kumar Bhagat, Tiyasha Tiyasha, Khaled Mohamed Khedher, Nadhir Al-Ansari, Shamsuddin Shahid, Zaher Mundher Yaseen
发表日期
2022/6/7
来源
Neurocomputing
卷号
489
页码范围
271-308
出版商
Elsevier
简介
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 …
学术搜索中的文章
H Tao, MM Hameed, HA Marhoon… - Neurocomputing, 2022