Avalanche Susceptibility Assessment and Dominant Factor Analysis in Southwestern Tibet Using Coupled Machine Learning Models and Multi-Source Data

Liang Yuhang; Li Xiaoning; Ling Sixiang; Sun Chunwei; Song Dianjun; Zhao Siyuan; Qin Zhuang'en

doi:10.12454/j.jsuese.202500655

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Avalanche Susceptibility Assessment and Dominant Factor Analysis in Southwestern Tibet Using Coupled Machine Learning Models and Multi-Source Data

更新时间：2026-01-23

- Avalanche Susceptibility Assessment and Dominant Factor Analysis in Southwestern Tibet Using Coupled Machine Learning Models and Multi-Source Data
- “In the field of avalanche disaster risk assessment on the Qinghai Tibet Plateau, experts have proposed a new method that integrates multi-source data and hybrid machine learning models, significantly improving the accuracy of model performance and providing scientific basis for avalanche risk prevention and control in high-altitude and cold regions.”
- Advanced Engineering Sciences Pages: 1-17(2026)
- 作者机构：
  
  1.西华大学应急管理学院，四川成都 610039
  2.西南交通大学地球科学与工程学院，四川成都 611756
  3.中铁第一勘察设计院集团有限公司，陕西西安710043
  4.四川大学，水利水电学院，山区河流保护与治理全国重点实验室，四川成都610065
  5.中国安能集团第三工程局有限公司，四川成都 611136
- 作者简介：
- 基金信息：
- DOI：10.12454/j.jsuese.202500655
  CLC： P642.2
- Published Online：23 January 2026，
- 稿件说明：
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梁宇航,李晓宁,凌斯祥等.耦合机器学习模型与多源数据的藏西南雪崩易发性评价及主控因素分析[J].工程科学与技术,

Liang Yuhang,Li Xiaoning,Ling Sixiang,et al.Avalanche Susceptibility Assessment and Dominant Factor Analysis in Southwestern Tibet Using Coupled Machine Learning Models and Multi-Source Data[J].Advanced Engineering Sciences,
梁宇航,李晓宁,凌斯祥等.耦合机器学习模型与多源数据的藏西南雪崩易发性评价及主控因素分析[J].工程科学与技术, DOI：10.12454/j.jsuese.202500655.

Liang Yuhang,Li Xiaoning,Ling Sixiang,et al.Avalanche Susceptibility Assessment and Dominant Factor Analysis in Southwestern Tibet Using Coupled Machine Learning Models and Multi-Source Data[J].Advanced Engineering Sciences, DOI：10.12454/j.jsuese.202500655.XXXX,XX(XX):1‒17.

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Avalanche Susceptibility Assessment and Dominant Factor Analysis in Southwestern Tibet Using Coupled Machine Learning Models and Multi-Source Data

DOI：10.12454/j.jsuese.202500655