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2024 Vol. 7, No. 2
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Tao Li, Chen-chen Xie, Chong Xu, Wen-wen Qi, Yuan-dong Huang, Lei Li, 2024. Automated machine learning for rainfall-induced landslide hazard mapping in Luhe County of Guangdong Province, China, China Geology, 7, 314-328. doi: 10.31035/cg2024064
Citation: Tao Li, Chen-chen Xie, Chong Xu, Wen-wen Qi, Yuan-dong Huang, Lei Li, 2024. Automated machine learning for rainfall-induced landslide hazard mapping in Luhe County of Guangdong Province, China, China Geology, 7, 314-328. doi: 10.31035/cg2024064
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Automated machine learning for rainfall-induced landslide hazard mapping in Luhe County of Guangdong Province, China

  • a.

    National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China

  • b.

    Key Laboratory of Compound and Chained Natural Hazards Dynamics, Ministry of Emergency Management of China, Beijing 100085, China

  • c.

    Institute of Disaster Prevention, Sanhe 065201, China

  • d.

    School of Emergency Management Science and Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

More Information
Received Date:  04 July 2023
Revised Date:  24 August 2023
Accepted Date:  11 March 2024
Available Online:  25 April 2024
Publish Date:  15 April 2024
    Abstract

  • Landslide hazard mapping is essential for regional landslide hazard management. The main objective of this study is to construct a rainfall-induced landslide hazard map of Luhe County, China based on an automated machine learning framework (AutoGluon). A total of 2241 landslides were identified from satellite images before and after the rainfall event, and 10 impact factors including elevation, slope, aspect, normalized difference vegetation index (NDVI), topographic wetness index (TWI), lithology, land cover, distance to roads, distance to rivers, and rainfall were selected as indicators. The WeightedEnsemble model, which is an ensemble of 13 basic machine learning models weighted together, was used to output the landslide hazard assessment results. The results indicate that landslides were mainly occurred in the central part of the study area, especially in Hetian and Shanghu. Totally 102.44 s were spent to train all the models, and the ensemble model WeightedEnsemble has an Area Under the Curve (AUC) value of 92.36% in the test set. In addition, 14.95% of the study area was determined to be at very high hazard, with a landslide density of 12.02 per square kilometer. This study serves as a significant reference for the prevention and mitigation of geological hazards and land use planning in Luhe County.

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