Volume 46 Issue 10
Nov.  2021
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Li Wenbin, Fan Xuanmei, Huang Faming, Wu Xueling, Yin Kunlong, Chang Zhilu, 2021. Uncertainties of Landslide Susceptibility Modeling under Different Environmental Factor Connections and Prediction Models. Earth Science, 46(10): 3777-3795. doi: 10.3799/dqkx.2021.042
Citation: Li Wenbin, Fan Xuanmei, Huang Faming, Wu Xueling, Yin Kunlong, Chang Zhilu, 2021. Uncertainties of Landslide Susceptibility Modeling under Different Environmental Factor Connections and Prediction Models. Earth Science, 46(10): 3777-3795. doi: 10.3799/dqkx.2021.042
shu

Uncertainties of Landslide Susceptibility Modeling under Different Environmental Factor Connections and Prediction Models

doi: 10.3799/dqkx.2021.042
  • 1.

    School of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, China

  • 2.

    State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China

  • 3.

    Institute of Geophysics & Geomatics, China University of Geosciences, Wuhan 430074, China

  • 4.

    Faculty of Engineering, China University of Geosciences, Wuhan 430074, China

  • Received Date: 2020-11-28
    Available Online: 2021-11-03
  • Publish Date: 2021-11-03
    Abstract
  • This study aims to explore the influences of some modeling factors including the non-linear correlation calculation between landslides and environmental factors and the different data-based models on the uncertainty law of landslide susceptibility prediction (LSP) modeling. The Ruijin City of Jiangxi Province in China with investigated 370 landslides and 10 environmental factors is used as study case. Accordingly, a total of 20 types of different coupling modeling conditions are proposed for LSP with five different connection methods(probability statistics (PS), frequency ratio (FR), information value (Ⅳ), index of entropy (IOE) and weight of evidence (WOE)) and four different data-based models including logistic regression (LR), back propagation neural networks (BPNN), support vector machines (SVM) and random forest (RF). Meanwhile, four single LR, BPNN, SVM and RF models with the original data as input variables are also proposed, as a whole, a total of 24 types of modeling conditions for LSP are obtained based on the above 20 types of coupling conditions and 4 types of single models. Finally, the uncertainty characteristics in the LSP modeling are assessed using the area under the receiver operation curve (ROC), mean value, standard deviation and significance test, respectively. Results show follows. (1) WOE-based models have the highest LSP accuracy and low uncertainty while PS-based models have the lowest LSP accuracy and the highest uncertainty, and the FR, Ⅳ and IOE-based models are in between. (2) The single data-based models have slightly lower LSP accuracies than those of the coupling models on the whole and cannot calculate the influence law of each sub-interval of environmental factors on landslide evolution, however, the single data-based models have higher modeling efficiency than those of the coupling models. (3) Among all the data-based models, RF model has the highest LSP accuracy and relatively low uncertainty, followed by the SVM, BPNN and LR models, respectively. It is concluded that the WOE is a very excellent correlation method and the RF model predicts the optimal LSP performance, the LSP results of WOE-RF model have the lowest uncertainties and the predicted landslide susceptibility indexes are more consistent with the actual landslides distribution characteristics.

     

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