Skip to main content

ORIGINAL RESEARCH article

Front. Earth Sci.
Sec. Geohazards and Georisks
Volume 12 - 2024 | doi: 10.3389/feart.2024.1444882
This article is part of the Research Topic Risk Assessment and Resilience of Extreme Weather-Induced Disasters View all 8 articles

Exploring Bayesian Network Model with Noise Filtering for Rainfallreduced Landslide Susceptibility Assessment in Fujian, China

Provisionally accepted
Suhua Zhou Suhua Zhou 1,2*Jinfeng Li Jinfeng Li 1,2Jiuchang Zhang Jiuchang Zhang 3*Zhiwen Xu Zhiwen Xu 1,2Xianzhui Lu Xianzhui Lu 4
  • 1 College of Civil Engineering, Hunan University, Changsha, Hunan Province, China
  • 2 Key Laboratory of Building Safety and Energy Efficiency, Ministry of Education, Hunan University, Changsha, Anhui Province, China
  • 3 Department of Civil Engineering, Yunnan Minzu University, Kunming, Yunnan Province, China
  • 4 Ministry of Land and Resources, Beijing, China

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

    Machine learning models have been increasingly popular in landslide susceptibility mapping based on the correlations among landslides and their inducing factors. However, mislabeled data in model training sets would deteriorate model accuracy. This study employed a Bayesian network to analyze influencing factors on landslides in Fujian Province, China, prone to typhoons and landslides. An inventory of 5992 historical landslides informs Bayesian network modeling, with ten geoenvironmental factors as predictors. We introduced a progressive noise filtering method to mitigate the mislabeling effects of non-landslide points. The results show that altitude, wind speed, and lithology are the most important factors of landslides in the study area. The accuracy of the resultant landslide susceptibility map was verified using the area under the receiver operating characteristic curve (AUC) and Moran's I index. The AUC value was improved from 0.838 to 0.931 during the progressive noise filtering. The correlation between historical landslide number density (LND) and resultant landslide susceptibility index (LSI) was evaluated. The Local Indicators of Spatial Association based on Moran's I index shows consistent distribution patterns for high LND and high LSI regions. This study provides a useful reference for reliable landslide susceptibility mapping in the study area and similar areas.

    Keywords: Landslide susceptibility mapping1, GIS2, Tropical cyclone3, Bayesian Network model4, Noise filtering5

    Received: 06 Jun 2024; Accepted: 07 Aug 2024.

    Copyright: © 2024 Zhou, Li, Zhang, Xu and Lu. 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:
    Suhua Zhou, College of Civil Engineering, Hunan University, Changsha, 410082, Hunan Province, China
    Jiuchang Zhang, Department of Civil Engineering, Yunnan Minzu University, Kunming, Yunnan Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.