| Snow avalanche susceptibility evaluation in the central Shaluli Mountains of Tibetan Plateau based on machine learning method北大核心CSCD |
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| 引用本文： | 文洪,巫锡勇,赵思远,边瑞,周桂宇,孟少伟,孙春卫.Snow avalanche susceptibility evaluation in the central Shaluli Mountains of Tibetan Plateau based on machine learning method北大核心CSCD[J].冰川冻土,2022,44(6):1694-1706. |
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| 作者姓名： | 文洪 巫锡勇 赵思远 边瑞 周桂宇 孟少伟 孙春卫 |
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| 作者单位： | 1.宜宾学院 智能制造学部，四川 宜宾 644007;2.西南交通大学 地球科学与环境工程学院，四川 成都 611756;3.四川大学 水利水电 学院 水力学与山区河流开发保护国家重点实验室，四川 成都 610065;4.中铁二院工程集团有限责任公司，四川 成都 610031 |
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| 基金项目： | 第二次青藏高原综合科学考察研究项目(2019QZKK0905);宜宾学院计算物理四川省高校重点实验室开放课题基金资助项目(412-2020JSWLYB001);宜宾学院科研培育项目(412-2020PY09) |
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| 摘 要： | Snow avalanches，which are widely and frequently developed at high elevations，seriously threatens the built traffic corridors in the Tibetan Plateau. Susceptibility evaluation of snow avalanche via machine learning model with a high forecast accuracy can be appled to quickly and effectively assess the regional avalanche risk. This paper took the central Shaluli Mountain region as the study area，in which the snow avalanche inventory was established through remote sensing interpretation and field investigation verification. We quantitatively extracted 17 evaluation factors via GIS-based analysis，and these factors were selected through the variance expansion factor（VIF）. Four machine learning models containing SVM，DT，MLP and KNN were used to compile the susceptibility index map of snow avalanches，and kappa coefficient and ROC curve were used to verify the accuracy. The results suggested that the susceptibility indexes obtained from SVM，DT，MLP and KNN were in the range of［0，0. 964］，［0，815］，［0，0. 995］and［0，1］，respectively. The accuracy test results show that these four models all have good prediction accuracy. Among them，the SVM model is the best. The results also indicated that the areas with the high snow avalanche susceptibility mainly distributed in Genie Mountain and Rigong Mountain，most of which were above the planation surface of the Tibetan Plateau. The average altitude of the extremely high snow-avalanche-prone areas is 4 939 m，while the average altitude of the high snow avalanche-prone areas is 4 859 m. The snow avalanche has low perniciousness on the Sichuan-Tibet Highway and the Sichuan-Tibet Railway in the study area. This study can provide theoretical basis and method reference for disaster prevention and mitigation of snow avalanche along Sichuan-Tibet Railway and other major projects across Shaluli Mountains region. © 2022 Science Press (China).
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| 关 键 词： | 雪崩 易发性评价 机器学习 沙鲁里山系 青藏高原 |
| 收稿时间： | 2021-05-16 |
| 修稿时间： | 2021-10-18 |
Snow avalanche susceptibility evaluation in the central Shaluli Mountains of Tibetan Plateau based on machine learning method |
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| Hong WEN,Xiyong WU,Siyuan ZHAO,Rui BIAN,Guiyu ZHOU,Shaowei MENG,Chunwei SUN.Snow avalanche susceptibility evaluation in the central Shaluli Mountains of Tibetan Plateau based on machine learning method[J].Journal of Glaciology and Geocryology,2022,44(6):1694-1706. |
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| Authors: | Hong WEN Xiyong WU Siyuan ZHAO Rui BIAN Guiyu ZHOU Shaowei MENG Chunwei SUN |
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| Institution: | 1.Faculty of Intelligence Manufacturing，Yibin University，Yibin 644000，Sichuan，China;2.Faculty of Geosciences and Environmental Engineering，Southwest Jiaotong University，Chengdu 611756，China;3.State Key Laboratory of Hydraulics and Mountain River Engineering，College of Water Resource & Hydropower，Sichuan University，Chengdu 610065，China;4.China Railway Eryuan Engineering Group Co. Ltd，Chengdu 610031，China |
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| Abstract: | Snow avalanches， which are widely and frequently developed at high elevations， seriously threatens the built traffic corridors in the Tibetan Plateau. Susceptibility evaluation of snow avalanche via machine learning model with a high forecast accuracy can be appled to quickly and effectively assess the regional avalanche risk. This paper took the central Shaluli Mountain region as the study area， in which the snow avalanche inventory was established through remote sensing interpretation and field investigation verification. We quantitatively extracted 17 evaluation factors via GIS-based analysis， and these factors were selected through the variance expansion factor （VIF）. Four machine learning models containing SVM， DT， MLP and KNN were used to compile the susceptibility index map of snow avalanches， and kappa coefficient and ROC curve were used to verify the accuracy. The results suggested that the susceptibility indexes obtained from SVM， DT， MLP and KNN were in the range of ［0，0.964］， ［0，815］， ［0，0.995］ and ［0，1］， respectively. The accuracy test results show that these four models all have good prediction accuracy. Among them， the SVM model is the best. The results also indicated that the areas with the high snow avalanche susceptibility mainly distributed in Genie Mountain and Rigong Mountain， most of which were above the planation surface of the Tibetan Plateau. The average altitude of the extremely high snow-avalanche-prone areas is 4 939 m， while the average altitude of the high snow avalanche-prone areas is 4 859 m. The snow avalanche has low perniciousness on the Sichuan-Tibet Highway and the Sichuan-Tibet Railway in the study area. This study can provide theoretical basis and method reference for disaster prevention and mitigation of snow avalanche along Sichuan-Tibet Railway and other major projects across Shaluli Mountains region. |
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| Keywords: | snow avalanche susceptibility evaluation machine learning Shaluli Mountains Tibetan Plateau |
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