1. Landslide Geo-Hazard Risk Mapping Using Logistic Regression Modeling in Guixi, Jiangxi, China
- Author
-
Yong Song, Tao Lang, Weicheng Wu, Lifeng Xie, Ziyu Lin, Wenheng Liu, Chongjian Shao, Xiaofeng Zhang, Xiangtong Liu, Yonghui Bai, Xiaolan Huang, Penghui Ou, Jie Li, Renxiang Chen, Ming Zhang, Guiliang Zhang, Xiaoting Zhou, Yaozu Qin, Yixuan Liu, Wenchao Huangfu, Yang Zhang, Xiao Fu, Jingheng Jiang, and Shanling Peng
- Subjects
risk prediction and mapping ,010504 meteorology & atmospheric sciences ,Landslide risk assessment ,Geography, Planning and Development ,TJ807-830 ,Management, Monitoring, Policy and Law ,TD194-195 ,010502 geochemistry & geophysics ,Logistic regression ,01 natural sciences ,Renewable energy sources ,certainty factor (CF) ,landslide hazard ,Cohen's kappa ,Statistics ,GE1-350 ,0105 earth and related environmental sciences ,Mathematics ,Training set ,Environmental effects of industries and plants ,Renewable Energy, Sustainability and the Environment ,Elevation ,Sampling (statistics) ,Landslide ,multiple logistic regression (MLR) ,Environmental sciences ,Risk map - Abstract
Reliable prediction of landslide occurrence is important for hazard risk reduction and prevention. Taking Guixi in northeast Jiangxi as an example, this research aimed to conduct such a landslide risk assessment using a multiple logistic regression (MLR) algorithm. Field-investigated landslides and non-landslide sites were converted into polygons. We randomly generated 50,000 sampling points to intersect these polygons and the intersected points were divided into two parts, a training set (TS) and a validation set (VT) in a ratio of 7 to 3. Thirteen geo-environmental factors, including elevation, slope, and distance from roads were employed as hazard-causative factors, which were intersected by the TS to create the random point (RP)-based dataset. The next step was to compute the certainty factor (CF) of each factor to constitute a CF-based dataset. MLR was applied to the two datasets for landslide risk modeling. The probability of landslides was then calculated in each pixel, and risk maps were produced. The overall accuracy of these two models versus VS was 91.5% and 90.4% with a Kappa coefficient of 0.814 and 0.782, respectively. The RP-based MLR modeling achieved more reliable predictions and its risk map seems more plausible for providing technical support for implementing disaster prevention measures in Guixi.
- Published
- 2021