Groundwater Level Assessment in an Alluvial Aquifer Using Neural Networks

Abstract

Groundwater is an important source of water worldwide due to its wide availability and generally good quality. Earlier groundwater was easily accessible to meet various domestic demands, but recently, it is vulnerable depletion in many areas due to over exploitation and mismanagement of groundwater resources. This study used the Artificial Neural Network (ANN) model to forecast groundwater (GW) level near Varanasi. ANN is a way to develop a prediction model based on the human brain's functions. This research provides a flawless prediction using the LM (Levenberg-Marquardt) and GDX training algorithms (Adaptive Learning rate with back Propagation). Data from eight wells, annual precipitation, the maximum and minimum temperatures, and relative humidity are all accepted as inputs, while the output is expected groundwater levels. The R (regression coefficient) and RMSE (root mean square error) values were used to measure model competency and precision. The observed R and RMSE values for the majority of the wells were heading towards unity using the LM technique. This LM technique is effective when we have a limited amount of data, and it is believed that this strategy will produce a precise result for a large amount of data. When there is a data constraint, the LM approach is found to be appropriate for determining any forecast of water fluctuations. This technique produces accurate results when the river location is used as an input in the artificial neural network (ANN).