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ORIGINAL RESEARCH article

Front. Environ. Sci.
Sec. Environmental Informatics and Remote Sensing
Volume 13 - 2025 | doi: 10.3389/fenvs.2025.1522949

A Landslide Susceptibility Assessment method using SBAS-InSAR to optimize Bayesian Network

Provisionally accepted
Xinyu Gao Xinyu Gao 1Bo Wang Bo Wang 1*Wen Dai Wen Dai 2*Yuanmin Liu Yuanmin Liu 3,4*
  • 1 School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing, China
  • 2 Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, Jiangsu Province, China
  • 3 School of Geography, Nanjing Normal University, Nanjing, Liaoning Province, China
  • 4 Key Laboratory of Virtual Geographic Environment, School of Geography, Nanjing Normal University, Nanjing, Liaoning Province, China

The final, formatted version of the article will be published soon.

    Landslide susceptibility assessment is crucial to mitigate the severe impacts of landslides. Although Bayesian network (BN) has been widely used in landslide susceptibility assessment, no study has compared the accuracy of different BN structure construction methods for this purpose. SBAS-InSAR technology plays a vital role in landslide research, but its advantages combined with BN to further improve prediction accuracy still need to be studied. This paper takes Hanyuan County as the study area. First, 20 traditional landslide impact factors were extracted from data such as topography and meteorology. A new method GDSP was designed to fuse GeoDetector and SHAP for dominant factor screening. Then, 8 different BN structure learning methods were compared using the AUC value of the ROC curve, among which Tabu&K2 method showed the highest accuracy. The deformation factor calculated by SBAS-InSAR is then incorporated into the BN model. The optimized Bayesian network (OPT-BN) outperformed the unoptimized version (ORI-BN) in accuracy, and the landslide susceptibility mapping was more reasonable. The reverse inference highlighted that areas with lower elevation, plow land, impervious cover, and higher rainfall are more prone to landslides. This method provides valuable insights into landslide hazard prevention and control and provides a new method for future landslide research.

    Keywords: Landslide susceptibility, Bayesian network, GeoDetector model, SHAP Interpreter, reverse inference

    Received: 05 Nov 2024; Accepted: 05 Feb 2025.

    Copyright: © 2025 Gao, Wang, Dai and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Bo Wang, School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing, China
    Wen Dai, Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, 210044, Jiangsu Province, China
    Yuanmin Liu, School of Geography, Nanjing Normal University, Nanjing, Liaoning Province, China

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