A new artificial intelligence strategy for predicting the groundwater level over the Rafsanjan aquifer in Iran

Abstract

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 method called Gradient Boosting Regression (GBR). In this way, the satellite-based products, including the Tropical Rainfall Measuring Mission (TRMM) and the Gravity Recovery and Climate Experiment (GRACE) datasets, as well as the pumping rate, are used as the predictive variables in different lag times. To obtain the optimal input combinations, the Gamma Test (GT) is employed as a non-linear feature selection technique. The spatial analysis of the performance prediction is performed using several error metrics (e.g., R2, and NRMSE). Results indicate that the GBR provides a high prediction performance to predict the GWL, whereas the GRACE product is an accurate predictive variable. The correlation analysis between the predicted and observed GWL shows that the coefficient of determination values vary in the range of 0.66 to 0.94 over the different lead times. The spatial pattern of the prediction accuracy indicates that the regions with higher water depth and pumping rate offer higher performance. Moreover, the increasing trends in performance accuracy are observed from the north to south and the west to east of the Rafsanjan aquifer. In general, the proposed approach provides a reliable insight for water resources planner to make a decision based on the accurate modeling results.