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 …

This study is a review to the special issue on artificial intelligence (AI) methods for
groundwater level (GWL) modeling and forecasting, and presents a brief overview of the …

Groundwater resources (GWR) play a crucial role in agricultural crop production, daily life,
and economic progress. Therefore, accurate prediction of groundwater (GW) level will aid in …

While the application of neural networks for groundwater level forecasting in general has
been investigated by many authors, the use of nonlinear autoregressive networks with …

Many recent studies propose wavelet-based hydrological and water resources forecasting
models that have been incorrectly developed and that cannot properly be used for real …

This study presents a new strategy to predict the monthly groundwater level with short-and
long-lead times over the Rafsanjan aquifer in Iran using an ensemble machine learning …

The nonlinear groundwater level fluctuations depend on the interaction of many factors such
as evapotranspiration, precipitation, groundwater abstraction, and hydrogeological …

Modeling the behavior of groundwater levels is necessary to implement sustainable
groundwater resource management. Groundwater is a non-linear and complex system …

The ability of the extreme learning machine (ELM) is investigated in modelling groundwater
level (GWL) fluctuations using hydro-climatic data obtained for Hormozgan Province …

The application of a nonlinear autoregressive modeling approach with exogenous input
(NARX) neural networks for modeling groundwater level fluctuation has been examined by …