Landslide Geo-Hazard Risk Mapping Using Logistic Regression Modeling in Guixi, Jiangxi, China

Wenchao Huangfu, Weicheng Wu, Xiaoting Zhou, Ziyu Lin, Guiliang Zhang, Renxiang Chen, Yong Song, Tao Lang, Yaozu Qin, Penghui Ou, Yang Zhang, Lifeng Xie, Xiaolan Huang, Xiao Fu, Jie Li, Jingheng Jiang, Ming Zhang, Yixuan Liu, Shanling Peng, Chongjian Shao, Yonghui Bai, Xiaofeng Zhang, Xiangtong Liu and Wenheng Liu
Additional contact information
Wenchao Huangfu: Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Weicheng Wu: Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Xiaoting Zhou: Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Ziyu Lin: Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Guiliang Zhang: 264 Geological Team of Jiangxi Nuclear Industry, Ganzhou 341000, China
Renxiang Chen: 264 Geological Team of Jiangxi Nuclear Industry, Ganzhou 341000, China
Yong Song: 264 Geological Team of Jiangxi Nuclear Industry, Ganzhou 341000, China
Tao Lang: 264 Geological Team of Jiangxi Nuclear Industry, Ganzhou 341000, China
Yaozu Qin: Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Penghui Ou: Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Yang Zhang: Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Lifeng Xie: Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Xiaolan Huang: Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Xiao Fu: Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Jie Li: Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Jingheng Jiang: Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Ming Zhang: Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Yixuan Liu: Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Shanling Peng: Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Chongjian Shao: Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Yonghui Bai: Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China
Xiaofeng Zhang: School of Geophysics and Measurement-Control Technology, East China University of Technology, Nanchang 330013, China
Xiangtong Liu: Faculty of Geomatics, East China University of Technology, Nanchang 330013, China
Wenheng Liu: Key Laboratory of Digital Lands and Resources and Faculty of Earth Sciences, East China University of Technology, Nanchang 330013, China

Sustainability, 2021, vol. 13, issue 9, 1-19

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.

Keywords: multiple logistic regression (MLR); certainty factor (CF); landslide hazard; risk prediction and mapping (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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