Groundwater level prediction using machine learning models: A comprehensive review

Developing accurate soft computing methods for groundwater level (GWL) forecasting is essential enhancing the planning and management of water resources. Over past two decades, significant progress has been made in GWL prediction using machine learning (ML) models. Several review articles have published, reporting advances this field up to 2018. However, existing do not cover several aspects simulations ML, which are scientists practitioners working hydrology resource management. The current article aims provide a clear understanding state-of-the-art ML models implemented modeling milestones achieved domain. includes all types employed from 2008 2020 (138 articles) summarizes details reviewed papers, including models, data span, time scale, input output parameters, performance criteria used, best identified. Furthermore, recommendations possible future research directions improve accuracy enhance related knowledge outlined.