Risk Assessment and Analysis of Its Influencing Factors of Debris Flows in Typical Arid Mountain Environment: A Case Study of Central Tien Shan Mountains, China

  • Zhi Li
  • , Mingyang Wu
  • , Ningsheng Chen*
  • , Runing Hou
  • , Shufeng Tian
  • , Mahfuzur Rahman
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

The Tien Shan Mountain range connects Central Asia with northwestern China and is a crucial transport junction between East and West Asia. It is a common location for regional debris flows, which pose a significant risk to ecological security and the safety of people and property. Nevertheless, limited knowledge exists about the distribution of disaster risks and the impacted populations. This study uses advanced machine learning techniques to identify the key natural and social factors influencing these hazards and incorporates the Social Vulnerability Index (SoVI) to assess societal vulnerability. The outcomes demonstrate that (1) the debris flow hazard in the Tien Shan Mountain area is primarily governed by the geological structure, which dictates the material source and, in turn, dictates the onset of debris flows. (2) The vulnerability demonstrates a high spatial tendency in the north and a low one in the south, with evident spatial clustering characteristics. (3) A total of 19.13% of the study area is classified as high-hazard, with specific distribution zones including the northern foothills of the Tien Shan Mountains, the low-mountain zones of the southern foothills of the Tien Shan Mountains, and the Yili Valley zone. This holistic approach offers valuable insights into the spatial distribution of risks, aiding in prioritizing disaster preparedness and mitigation efforts. Also, our findings and conclusions are beneficial for local decision makers to allocate resources effectively and promote sustainable development practices in the region.

Original languageEnglish
Article number5681
JournalRemote Sensing
Volume15
Issue number24
DOIs
StatePublished - Dec 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 by the authors.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 1 - No Poverty
    SDG 1 No Poverty
  2. SDG 4 - Quality Education
    SDG 4 Quality Education
  3. SDG 8 - Decent Work and Economic Growth
    SDG 8 Decent Work and Economic Growth
  4. SDG 17 - Partnerships for the Goals
    SDG 17 Partnerships for the Goals

Keywords

  • Tien Shan Mountain
  • debris flow
  • machine learning
  • risk analysis

ASJC Scopus subject areas

  • General Earth and Planetary Sciences

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