Hydro-environmental predictive management of sub-surface salinization in arid nearshore-coastal saline aquifer using deep learning and SHAP analysis

Groundwater (GW) management is vital in arid regions like Saudi Arabia, where agriculture heavily depends on this resource. Traditional GW monitoring and prediction methods often fall short of capturing the complex interactions and temporal dynamics of GW systems. This study introduces an innovative approach that integrates deep learning (DL) techniques with Shapley Additive Explanations (SHAP) to enhance GW predictive management in Saudi Arabia’s agricultural regions. SHAP analysis is used to interpret each feature’s influence on the model’s predictions, thereby improving the transparency and understanding of the models’ decision-making processes. Six different data-driven models, including Hammerstein-Wiener (HW), Random Forest (RF), Artificial Neural Networks (ANNs), eXtreme Gradient Boosting (XGBoost), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM), were utilized to predict GW salinity based on electrical conductivity (EC). The calibration results suggest that the RF model exhibits the highest Determination Coefficient (DC) of 0.9903 and Nash-Sutcliffe Efficiency (NSE) of 0.9899, indicating its superior predictive accuracy, followed closely by the LSTM model with a DC of 0.9835 and NSE of 0.9827. During the validation phase, the LSTM model demonstrated superior performance with the lowest Mean Absolute Error (MAE) of 13.9547 and Mean Absolute Percentage Error (MAPE) of 0.2813, indicating minimal deviation between predicted and observed EC values. The SHAP analysis revealed that chloride (Cl), with a mean SHAP value of ~ 1250, has the highest impact on EC, suggesting that variations in chloride concentration significantly influence GW salinity. Magnesium (Mg) follows closely with a mean SHAP value of ~ 1200, highlighting its role in water hardness and EC. Sodium (Na), with a mean SHAP value of ~ 600, has a moderate impact, contributing to overall salinity from natural processes and human activities. The proposed method has proven effective, with the LSTM algorithm offering an excellent and reliable tool for predicting EC. This advancement will result in more efficient planning and decision-making related to water resources.