Modelling of River-Groundwater Interactions under Rainfall Events Based on a Modified Tank Model

A multitank model experiment is employed to simulate the river-groundwater interaction under rainfall events. These experiments involve coarse and fine materials and rainfall events of 45 and 65 mm/hr. We developed a modified tank model for estimation of the groundwater table and river levels in these experiments. Parameter training of our tank model includes two algorithms: (i) the nonincremental learning algorithm-based model can predict the pore water pressure (PWP) in a slope and river under a 65 mm/hr rainfall event (coarse material) with Nash–Sutcliffe efficiency (NSE) = 0.427 and −0.909 and (ii) the incremental learning algorithm-based model can predict the PWP in a slope and river with NSE = 0.994 and 0.995. Then, the river-groundwater interaction was reproduced by a numerical case. The results of the deterministic method of the numerical case and optimized method of the modified tank model matched well.

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