Machine Learning-Based Evaluation of Susceptibility to Geological Hazards in the Hengduan Mountains Region, China

Abstract

The Hengduan Mountains Region (HMR) is one of the areas that experience the most frequent geological hazards in China. However, few reports are available that address the geological hazard susceptibility of the region. This study developed six machine learning models to assess the geological hazard susceptibility. The results show that areas with medium and high susceptibility to geological hazards as a whole account for almost 21% of the total area, while both are 18% when it comes to the single hazard of landslide and rockfall respectively. Medium and high geological hazard susceptibility is found in three parts of the HMR with different characteristics: (1) the central and southern parts, where the population of the region concentrates; (2) the northern part, where higher geological hazard susceptibility is found along the mountain ranges; and (3) the junction of Tibet, Yunnan, and Sichuan in the eastern part, which is prone to larger-scale geological hazards. Of all the potential influencing factors, topographic features and climatic variables act as the major driving factors behind geological hazards and elevation, slope, and precipitation are crucial indicators for geological hazard susceptibility assessment. This study developed the geological hazard susceptibility maps of the HMR and provided information for the multi-hazard risk assessment and management of the region.

Keywords

Geological hazards / Landslides / Machine learning techniques / Rockfalls / Susceptibility evaluation

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