Comparison of a physical model and phenomenological model to forecast groundwater levels in a rainfall-induced deep-seated landslide

Abstract

Increases in groundwater levels play an important role in triggering deep-seated landslides. As such, it is important to be able to accurately predict variations in groundwater levels. In order to select a more suitable method for the prediction of groundwater levels in relation to deep-seated landslides, a comparative study was conducted between a physical model and phenomenological model. The physical model is a finite-element seepage code named Slide by Rocscience. The phenomenological model is a Particle Swarm Optimization Support Vector Machine (PSO-SVM). In order to obtain more accurate calculated results from the physical seepage model, the input parameters of the physical seepage model were calibrated by using a trial-error method to compare the computed results with actual monitoring data. The input data of the phenomenological model were also processed in order to obtain more accurate calculated results. The results showed that the physical seepage model was difficult to well calibrate in the condition of less data and hence performed poorly. The validation results showed that the phenomenological model performed better. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of phenomenological model were 0.052 m and 0.043 m, respectively, compared with the values of physical seepage model were 0.92 m and 0.81 m, respectively. It means if constructing a satisfactory physical seepage model was difficult or understanding of physical process could not be considered, the phenomenological model might be sufficient.