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Abstract

Due to the difficulties in estimating groundwater recharge and cross-boundary nature of many aquifers, estimating groundwater recharge at large scale has been called upon. Process-based models as well as data-driven models have been established to meet this need. Meanwhile, with the advent of explainable artificial intelligence (XAI) methods, data-driven machine learning models can take advantage of enhanced explainability while keeping the strength of high flexibility. In this study, an ensemble neural network model was built to check the suitability of the model to predict groundwater recharge and the possibility to gain new insights from large data set. Recent large inputs of groundwater recharge data and additional input for the Arabian Peninsula collated in this study were fed to the model with multiple predictors related to climatology considering seasonality, soil and plant characteristics, topography, and hydrogeology. The model showed higher performance (adjusted R2: 0.702, RMSE: 193.35 mm yr−1) than a recent global process-based model in predicting groundwater recharge. Using XAI methods as individual conditional expectations and Shapley Additive Explanation interaction values, the model behavior was analyzed and possible linear and non-linear relationships between the predictors and the groundwater recharge rate were found. Long-term averaged precipitation and enhanced vegetation index showed non-linear relationships with groundwater recharge rate, while slope, compound topographic index, and water table depth showed low importance to the model results. Most model behaviors followed the domain knowledge, while multi-correlation between predictors and data skewness hindered the model from learning.