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ORIGINAL RESEARCH article

Front. Earth Sci.
Sec. Geohazards and Georisks
Volume 12 - 2024 | doi: 10.3389/feart.2024.1431203
This article is part of the Research Topic Prevention, Mitigation, and Relief of Compound and Chained Natural Hazards Volume II View all 5 articles

Examining the contribution of lithology and precipitation to the performance of earthquake-induced landslide hazard prediction

Provisionally accepted
Hui Wang Hui Wang 1Wei Wu Wei Wu 2*Wentao Yang Wentao Yang 1Meiyu Liu Meiyu Liu 2*
  • 1 School of Soil and Water Conservation, Beijing Forestry University, Haidian, Beijing, China
  • 2 National Disaster Reduction Center of China, Beijing, Beijing Municipality, China

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

    Earthquake-induced landslides (EQIL) are one of the most catastrophic geological hazards. Immediate and swift evaluation of EQIL hazard in the aftermath of an earthquake is critically important and of substantial practical value for disaster reduction. The selection of influencing factor layers is crucial when using machine learning methods to predict EQIL hazard. As important input factors for EQIL hazard models, lithology and precipitation are extensively employed in forecasting EQIL hazard. However, few work explored whether these layers can improve the accuracy of EQIL hazard predictions. With Random Forest (RF) models, we employed a traditional and a state-of-the-art sampling strategy to assess EQIL modelling with and without lithology and precipitation data for the 2022 Luding earthquake in China. First, by excluding both factors, we used eight other influencing factors (land use, slope aspect, slope, elevation, distance to faults, distance to rivers, NDVI, and peak ground acceleration) to generate a landslide hazard map. Second, lithology and precipitation were separately added to the original EQIL hazard models. The results indicate that neither lithology nor precipitation have positive effects on the prediction of EQIL for both sampling strategies. The high-risk areas (or low-risk areas) tend to cluster within certain lithology types or precipitation ranges, which significantly affects the accuracy of the hazard map. Additionally, the model with the state-of-the-art sampling strategy deteriorates more than the model with the traditional sampling strategy. We believe this is very likely due to the strong spatial clustering of negative sample points caused by the latest sampling strategy. Our findings will contribute to the assessment of postearthquake landslide hazards and the advancement of emergency disaster mitigation efforts.

    Keywords: Earthquake-induced landslide, Hazard prediction, lithology, precipitation, Luding

    Received: 11 May 2024; Accepted: 18 Sep 2024.

    Copyright: © 2024 Wang, Wu, Yang 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:
    Wei Wu, National Disaster Reduction Center of China, Beijing, 100000, Beijing Municipality, China
    Meiyu Liu, National Disaster Reduction Center of China, Beijing, 100000, Beijing Municipality, 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.