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

Author

Listed:
  • 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)

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.

Suggested Citation

  • 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 &, 2021. "Landslide Geo-Hazard Risk Mapping Using Logistic Regression Modeling in Guixi, Jiangxi, China," Sustainability, MDPI, vol. 13(9), pages 1-19, April.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4830-:d:543319
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    References listed on IDEAS

    as
    1. Agus Muntohar & Hung-Jiun Liao, 2010. "Rainfall infiltration: infinite slope model for landslides triggering by rainstorm," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 54(3), pages 967-984, September.
    2. Anna Roccati & Guido Paliaga & Fabio Luino & Francesco Faccini & Laura Turconi, 2021. "GIS-Based Landslide Susceptibility Mapping for Land Use Planning and Risk Assessment," Land, MDPI, vol. 10(2), pages 1-28, February.
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    Cited by:

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