LAUSR

Abstract Rainfall thresholds of landslides often are determined by empirical meteorological thresholds, but the reliability of this approach sometimes is limited by the lack of information about the hydrological processes that trigger landslides. Groundwater plays a critical role in triggering deep-seated landslides. In this study, we propose a methodology to estimate the rainfall threshold for a deep-seated landslide based on an integrated model that combines a model for predicting the level of the groundwater with a finite-element, strength-reduction model. First, in order to obtain more accurate results when predicting the level of the groundwater, a method is proposed to estimate the groundwater level fluctuation caused by rainfall (GLFR). Then, two different machine learning methods, i.e., the genetic algorithm back-propagation neural network (GA-BPNN) method and the genetic algorithm support vector machine (GA-SVM) method, are proposed for predicting the GLFR of the Duxiantou landslide located in Zhejiang Province, China. The results of the predictions showed that the performance of the GA-SVM model was better than that of the GA-BPNN model. Then, a series of numerical simulations was conducted to investigate the factor of safety (Fs) of the slope at different groundwater levels. At last, the probabilities of the occurrences of Duxiantou landslides for different return periods of rainfall intensity were evaluated to determine the rainfall threshold.