基于机器学习法的青藏高原沙鲁里山系中段雪崩易发性评价研究

Snow avalanche susceptibility evaluation in the central Shaluli Mountains of Tibetan Plateau based on machine learning method

青藏高原广泛发育、暴发频繁的雪崩对既有交通廊道造成严重威胁。采用高预测精度的机器学习算法对该类区域雪崩易发性进行评价,可快速、有效地对雪崩风险进行区域性评估。以青藏高原沙鲁里山系中段山区雪崩为研究对象,通过室内解译与现场验证相结合的方式识别并建立雪崩编目数据库,同时采用GIS、遥感等定量化提取技术,通过方差膨胀因子(VIF)筛选出14个评价因子,在此基础上利用支持向量机(SVM)、决策树(DT)、多层感知器(MLP)、K最邻近法(KNN)共4种机器学习模型对雪崩易发性进行评价并完成指数图的绘制,并采用Kappa系数和ROC曲线进行准确性检验。评价结果显示,SVM、DT、MLP、KNN的易发性指数分别在[0,0.964]、[0,815]、[0,0.995]、[0,1]范围内。精度检验结果显示这4种模型均具有较好或很好的预测精度,其中SVM模型的Kappa系数和AUC值均为最高,其AUC值高达0.912。结果表明研究区内雪崩易发性高的区域主要分布在夷平面以上的格聂山、日拱山等地,极高易发区平均海拔约4 939 m,高易发区平均海拔约4 859 m。该区域雪崩对川藏公路和在建的川藏铁路影响较小。该研究可为横穿沙鲁里山系的川藏铁路等重大工程建设的雪崩防灾减灾工作提供科学依据和方法借鉴。

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.