This review paper closely explores the techniques and significances of the most potent
artificial intelligence (AI) approaches in a concise and integrated way, specifically in the …

Fast and accurate identification of unknown pollution sources is a crucial and challenging
task in water resources management, in which the characteristics of unknown pollution …

Accurate information about groundwater level prediction is crucial for effective planning and
management of groundwater resources. In the present study, the Artificial Neural Network …

In identifying groundwater contaminant sources, given that the simulation model is
computationally inefficient, an ensemble surrogate model is proposed to improve the …

This study proposes a linked simulation–optimization model for solving the unknown
groundwater pollution source identification problems. In the proposed model, MODFLOW …

Forty years and 157 papers later, research on contaminant source identification has grown
exponentially in number but seems to be stalled concerning advancement towards the …

As the incidence frequency of groundwater pollution increases, many methods that identify
source characteristics of pollutants are being developed. In this study, a simulation …

In this study, a new simulation–optimization approach is proposed for solving the areal
groundwater pollution source identification problems which is an ill-posed inverse problem …

Identifying groundwater contaminant sources involves a reverse determination of the source
characteristics by monitoring contaminant concentrations in a few observation wells …

The application of artificial neural network (ANN) to rainfall-runoff simulations has provided
promising results in recent years. However, it is difficult to obtain satisfying results by using …