| Citation: | QU Pengxin，XIE Wanli，LIU Qiqi，et al. Research on Collapse and Landslide Risk Assessment Based on Machine Learning Improved IVM-RF Coupling Method：A Case Study of Zhidan County,Yan'an City[J]. Bulletin of Geological Science and Technology，2025，44（3）：1-16 doi: 10.19509/j.cnki.dzkq.tb20240583
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In order to provide data support for disaster prevention and mitigation and risk management in Zhidan County, and provide reference for risk assessment in similar areas, and to supplement the gap of not considering the influence of cumulative rainfall in the risk assessment of collapse and landslide disasters. The risk assessment was carried out under four different rainfall conditions of 10 years, 20 years, 50 years and 100 years. Taking Zhidan County as the research area, taking the grid unit as the evaluation unit, combined with the disaster characteristics and regional disaster background, through the Pearson correlation coefficient method, eight evaluation factors were selected, including elevation, slope, aspect, curvature, rock and soil type, distance from the river, distance from the road, and normalized vegetation index. The information model was used to evaluate the susceptibility and analyze the correlation between the disaster-causing factors and the disaster distribution. Using computer language, the analysis, transformation, management and drawing of the previous factor data are automatically processed, the information value method（IVM）-random forest（RF）coupling model is improved, and the automatic cycle iteration comparison selection of the model is realized. The accuracy of the two susceptibility models is compared by ROC curve. Based on the evaluation results of the coupled model, the risk assessment was carried out. The Pearson type Ⅲ curve was used to estimate the rainfall in the study area under four different conditions of 10 years, 20 years, 50 years and 100 years, and the risk zoning was carried out. For the results of susceptibility zoning, the AUC value of the evaluation results of the information-random forest coupling model is 0.87, which is better than the evaluation results of the IVM model. For the results of risk zoning, the area of high and extremely high risk areas has gradually increased from ten years to one hundred years. The improved coupling model evaluation method not only simplifies the operation but also improves the accuracy. According to the actual survey results, the coupling model does have better evaluation accuracy and prediction ability.
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