Fast zoning of rainfall-induced shallow landslide susceptibility based on physical process uncertainty：development and application of GIS-FORM
JI Jian1，CUI Hongzhi1，2，TONG Bin1，LYU Qing3，GAO Yufeng1
(1. Geotechnical Research Institute，Hohai University，Nanjing，Jiangsu 211000，China；2. Department of Civil and Environmental Engineering，UPC BarcelonaTECH，Barcelona 08034，Spain；3. College of Architecture and Engineering，Zhejiang University，Hangzhou，Zhejiang 310058，China)
Abstract：Efficient mapping of rainfall-induced landslide susceptibility is crucial to the success of regional-scale landslide prediction and the early warning of geological hazards. In this paper，a physically-based probabilistic modelling tool，herein named the probabilistic rainfall-induced landslide using simplified transient infiltration model (PRL-STIM)，was proposed to deal with the fast mapping of landslide susceptibility at regional scales. This modelling tool integrates the infinite slope model with considerations of rainfall-induced pore water pressure(PWP) and surface runoff. The first-order reliability method(FORM) for efficiently performing probabilistic computations is employed to simulate the geotechnical and geological uncertainties. The proposed PRL-STIM v1.0 tool is developed based on the Python programming language integrating with the Geographic Information System(GIS) framework. Validation of the proposed model is illustrated by an engineering case study of the rainfall-induced regional shallow landslides that occurred in July 2013 in Niangniangba，Gansu Province，China. The analysis results demonstrate that the adoption of the 50% failure probability threshold can effectively characterize the region?s landslide hazard susceptibility distribution. High-risk landslide areas can be well identified, with deterministic and probabilistic prediction accuracies reaching 79% and 81%，respectively，when a 20m buffer zone is used. Furthermore, it is shown that the probabilistic prediction accuracy of the rainfall-induced landslide susceptibility by PRL-STIM achieves 75%，surpassing the 72% prediction accuracy of the TRIGRS model based on the Richards equation, and it is worth noting that non-normal distributions of random geotechnical parameters may exert a significant influence on the predicted landslide susceptibility.
姬 建1，崔红志1，2，佟 斌1，吕 庆3，高玉峰1. 基于物理过程不确定性的降雨诱发浅层滑坡易发性快速区划：GIS-FORM技术开发与应用[J]. 岩石力学与工程学报, 2024, 43(4): 838-850.
JI Jian1，CUI Hongzhi1，2，TONG Bin1，LYU Qing3，GAO Yufeng1. Fast zoning of rainfall-induced shallow landslide susceptibility based on physical process uncertainty：development and application of GIS-FORM. , 2024, 43(4): 838-850.
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