Groundwater level prediction with machine learning for the Vidisha district, a semi-arid region of Central India

Abstract

Groundwater is one of the most important freshwater resources, particularly in dry and semi-arid regions with low annual precipitation and frequent droughts. For the planning and the management of water resources, this is very essential to generate a soft computing method that can predict or forecast groundwater table (GWT). The different machine and deep learning algorithms are evaluated in this research work to predict the groundwater table using season, monthly rainfall, and evapotranspiration rate, which are further compared. The performance evaluation of the different algorithms was done using five different statistical parameters, i.e., mean absolute error (MAE), mean squared error (MSE), root-mean-square error (RMSE), R2−score, and Nash-Sutcliffe Efficiency (NSE). From the results of this study, it is concluded that the Multivariate polynomial regression algorithm is the most efficient for predicting the groundwater table with the minimum mean absolute error in the range of 0.63–0.85 and R2-score of 0.82–0.88.