An innovative partition method for predicting shallow landslides by combining the slope stability analysis with a dynamic neural network model

Abstract

The objective of this study is to explore the relevance between the rainfall pattern, slope stability of the soil layer, and the occurrence of shallow landslides. A seepage flow model for subsurface flow simulation with a slope stability analysis approach was established to examine the temporal and spatial variation of unstable grids. To define the threshold conditions to trigger shallow landslides for different regions in the watershed, the spatial distribution of the safety factor at the initial state was derived and adopted as the reference basis to classify all grids of the watershed into multiple zones. The first novelty of this study is to apply such a partition, depending on the watershed topography and soil characteristics, to train the landslide prediction model separately. The second novelty is to execute a dynamic recurrent neural network (RNN) model to determine the possible duration and the start time of shallow landslides for each zone by considering the rainfall condition as well as the cumulative area of unstable grids, which can be obtained by performing the seepage flow model. In this way, the physical significance of the RNN prediction model can be reinforced. The analysis results showed that the proposed methodology could effectively track the respective period of occurring shallow landslides for each zone, additionally, only some specific zones in the watershed were necessary to be investigated because they were prone to cause a large number of grids to become unstable during rainstorms.