Enhancing groundwater level prediction accuracy using interpolation techniques in deep learning models

Abstract

Groundwater surface (GWS), which denotes the vertical extent of the water table or the volume of subterranean water within geologic formations, is pivotal for effective groundwater resource management. Accurately predicting GWS requires comprehensive and precise data to fully understand the influencing factors. The inherent temporal complexity and often incomplete datasets of GWS pose significant challenges to accurate assessments. This research aims to devise a comprehensive method that merges interpolation and prediction techniques to develop a functional model and dynamic system for GWS prediction. The study was conducted on the Azarshahr Plain aquifer in Iran, involving 34 observation wells with partially or entirely missing data. Initial analysis utilized three interpolation methods—Kriging, Support Vector Machine (SVM), and M5P—with the M5P method emerging as the most accurate, evidenced by the lowest Root Mean Square Error (RMSE) of 1.83. Two subsequent scenarios were examined: (1) using the M5P method to interpolate missing data for all 34 wells, and (2) using only data from 15 wells with complete records. GWS levels were predicted using Deep Neural Network (DNN) and Convolutional Neural Network (CNN) models. Comparative analysis highlighted the superior performance of the CNN model in both scenarios, particularly noting its effectiveness in GWS prediction. The improvement of data quality through interpolation significantly enhanced predictive accuracy by approximately 90 percent, thereby increasing the reliability of the predictive models for future groundwater management decisions.