Multiple Statistical Models Based Analysis of Causative Factors and Loess Landslides in Tianshui City, China

2017 5th International Conference on Environment Pollution and Prevention

生态环境科学

Su, Xing^1,2 ; Meng, Xingmin^1 ; Ye, Weilin^1,2 ; Wu, Weijiang^2 ; Liu, Xingrong^2 ; Wei, Wanhong^2

Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou

730000, China^1

Geological Hazards Prevention Institute, Gansu Academy of Sciences, Lanzhou

73000, China^2

关键词: Certainty factors;    Correlation analysis;    Geological conditions;    Information quantity;    Loess landslides;    Mountainous cities;    Statistical probability;    Weight of evidences;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/120/1/012013/pdf
DOI  :  10.1088/1755-1315/120/1/012013

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Tianshui City is one of the mountainous cities that are threatened by severe geo-hazards in Gansu Province, China. Statistical probability models have been widely used in analyzing and evaluating geo-hazards such as landslide. In this research, three approaches (Certainty Factor Method, Weight of Evidence Method and Information Quantity Method) were adopted to quantitively analyze the relationship between the causative factors and the landslides, respectively. The source data used in this study are including the SRTM DEM and local geological maps in the scale of 1:200,000. 12 causative factors (i.e., altitude, slope, aspect, curvature, plan curvature, profile curvature, roughness, relief amplitude, and distance to rivers, distance to faults, distance to roads, and the stratum lithology) were selected to do correlation analysis after thorough investigation of geological conditions and historical landslides. The results indicate that the outcomes of the three models are fairly consistent.