Quantifying Water Content of a Landfill With ERT Data by Bayesian Evidential Learning

Abstract Accurate estimation of water storage in municipal solid waste landfills is critical for assessing leachate‐generation risk yet remains challenging due to pronounced heterogeneity. Here we apply Bayesian Evidential Learning (BEL) to directly relate Electrical Resistivity Tomography (ERT) data to total water storage (TWS), bypassing explicit inversion. A semi‐parametric forward model generates 100,000 synthetic TWS–ERT pairs spanning stochastic saturation fields and petrophysical uncertainty. A Bayesian neural network captures data‐dependent predictive uncertainty, while stratified resampling and adaptive weighting mitigate class imbalance across the TWS range. The framework yields well‐calibrated posterior estimates and consistent agreement with independent water‐balance benchmarks from four field transects. The BEL–ERT workflow provides a rapid, open‐source alternative for landfill monitoring and highlights the potential of uncertainty‐aware learning from synthetic ensembles to quantify water storage in heterogeneous near‐surface systems.