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Modeling landslide susceptibility using an evidential belief function-based multiclass alternating decision tree and logistic model tree

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Abstract

The primary objective of the present research is to apply and compare the performance of evidential belief function (EBF)-based logistic model trees (LMTs) and multiclass alternate decision trees (LADTrees) in landslide susceptibility mapping in Xiaojin County, China. Firstly, 328 landslides were mapped in the study area. Then, 70% of landslide points were used as training samples randomly, and the remaining 30% were intended for validation samples. For the study area, 12 landslide-related conditioning factors were identified, for instance, plan curvature, profile curvature, elevation, slope angle, slope aspect, normalized difference vegetation index (NDVI), topographic wetness index (TWI), land use, lithology, distance to river soil, and distance to roads. The following procedure was to map landslide susceptible regions through EBF, LADTree and LMT models. Finally, the receiver operating characteristic (ROC) curve was utilized to contrast and test the capacity of the three models. The success rates with the training dataset were 0.880, 0.877 and 0.886 for the EBF, LADTree and LMT models, respectively. In addition, their prediction rates with the validation dataset were 0.846, 0.861 and 0.865, respectively. The results could provide references for disaster management and land-use planning.

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Funding

This study was supported by the Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2019JLM-7, Program No. 2020JQ-747).

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Authors and Affiliations

  1. College of Geology and Environment, Xi’an University of Science and Technology, Xi’an, 710054, China

    Qifei Zhao, Wei Chen & Huiyuan Bian

  2. Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Natural Resources, Xi’an, 710021, China

    Wei Chen

  3. Sichuan Institute of Geological Engineering Investigation Group Co. Ltd., Chengdu, 610072, China

    Chaohong Peng

  4. Institute of Water Conservancy Works Design of Xuzhou, Xuzhou, 221002, China

    Danzhi Wang

  5. Shaanxi Coal and Chemical Technology Institute Co. Ltd., Xi’an, 710065, China

    Weifeng Xue

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  1. Qifei Zhao
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Zhao, Q., Chen, W., Peng, C. et al. Modeling landslide susceptibility using an evidential belief function-based multiclass alternating decision tree and logistic model tree. Environ Earth Sci 81, 404 (2022). https://doi.org/10.1007/s12665-022-10525-3

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