关键词: 滑坡易发性, 评价因子, 随机森林模型, 信息量模型, 加权信息量模型, 西安市临潼区

Abstract: A weighted information value (WIV) model integrating random forest (FR) model and information value (IV) model is proposed in an attempt to improve the precision and accuracy of landslide susceptibility assessment. Lintong District of Xi'an City is taken as the research area. According to field survey and factor correlation analysis, ten factors including slope gradient, slope aspect, elevation, curvature, topographic relief, rock and soil type, rainfall, fault, water system, and road are selected as influence factors for the assessment. With 72 landslide hazard points as sample data, the landslide susceptibility is assessed using RF model, IV model and WIV model, respectively. Results demonstrate that the success rate and prediction rate of WIV model training samples are higher than those of FR model by 4.90% and 1.90%, respectively, and higher than those of IV model by 7.80% and 4.70%, respectively. Highly and extremely highly susceptible areas are mainly distributed in faults, rivers and roads. The rate of landslide hazard points in highly and extremely highly susceptible areas by WIV model is 6.37% and 4.44% higher than that by IV model and FR model, respectively. In conclusion, the results of WIV model are consistent with the actual situation of the research area.

Key words: landslide susceptibility, assessment factor, random forest model, information value model, weighted information value model, Lintong District of Xi'an City