Groundwater level prediction using machine learning models: A comprehensive review
Universiti Teknologi MARA · Ankang University · +21 more institutions
Abstract
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…
Citation impact
- FWCI
- 31.33
- Percentile
- 100%
- References
- 341
Authors
24- THTao HaiCorresponding
Universiti Teknologi MARA, Ankang University, Baoji University of Arts and Sciences
- MMMohammed Majeed Hameed
University of Al Maarif
- HAHaydar Abdulameer Marhoon
Thi Qar University, University of Kerbala, Al-Ayen Iraqi University
- MZMohammad Zounemat‐Kermani
Shahid Bahonar University of Kerman
- SHSalim Heddam
University of Skikda
Topics & keywords
- Computer science
- Field (mathematics)
- Machine learning
- Predictive modelling
- Resource (disambiguation)
- Scale (ratio)
- Groundwater resources
- Water resources
- Clean water and sanitation