reviewNeurocomputingMar 14, 2022HYBRID OA

Groundwater level prediction using machine learning models: A comprehensive review

Universiti Teknologi MARA · Ankang University · +21 more institutions

Indexed incrossref

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

412
total citations
FWCI
31.33
Percentile
100%
References
341
Citations per year

Authors

24

Topics & keywords

Keywords
  • Computer science
  • Field (mathematics)
  • Machine learning
  • Predictive modelling
  • Resource (disambiguation)
  • Scale (ratio)
  • Groundwater resources
  • Water resources
UN Sustainable Development Goals
  • Clean water and sanitation
No related works found for this paper.