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Agricultural water demand, groundwater extraction, surface water delivery and climate have complex nonlinear relationships with groundwater storage in agricultural regions. As an alternative to elaborate computationally intensive physical models, machine learning methods are often adopted as surrogate to capture such complex relationships due to their high computational efficiency. Inevitably, using only one machine learning model is prone to underestimate prediction uncertainty and subjected to poor accuracy. This study presents a novel machine learning-based groundwater ensemble modeling framework in conjunction with a Bayesian model averaging approach to predict groundwater storage change and improve overall model predicting reliability. Three different machine learning models have been developed namely artificial neural network, support vector machine and response surface regression. To …