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COMPARISON OF SNOW AVALANCHE SUSCEPTIBILITY ASSESSMENT AND POTENTIAL SNOW AVALANCHE RELEASE AREAS IDENTIFICATION ALONG YINING-AKSU RAILWAY, XINJIANG TIANSHAN MOUNTAINS
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摘要: 准确识别雪崩潜在释放区或雪崩高易发区域对高寒山区工程建设减灾防灾意义重大，特别是在雪崩监测数据缺失地区，能够提供重要的区域性灾害风险预估参考。本文以新疆天山地区伊阿铁路沿线区域为例，将铁路沿线154个雪崩范围形成区作为评价样本，开展基于机器学习算法的雪崩易发性评价，构建新疆天山地区伊阿铁路沿线雪崩易发性评价体系；开展基于数据叠加的雪崩潜在释放区(PRA)识别，绘制伊阿铁路沿线雪崩潜在释放区分布图，并对两个结果通过Kappa系数和AUC值进行检验，并对比讨论。结果显示，支持向量机(SVM)、多层感知器(MLP)、PRA的Kappa系数分别为0.806、0.774、0.600；AUC值分别为0.993、0.961、0.802，机器学习算法在雪崩易发性评价中的表现优于传统的基于数据叠加的雪崩潜在释放区识别算法；两种机器学习算法模型均精度高，其中支持向量机(SVM)算法表现最佳，优于多层感知器(MLP)，评价结果比较符合野外雪崩发育实际情况，可为高寒山区重大工程建设的雪崩防灾减灾工作提供基础的科学依据；雪崩潜在释放区的自动识别算法评价能力较弱，评价结果基本符合野外雪崩发育实际情况，对于缺乏可用数据的高寒山区具有评价意义。Abstract: Accurate identification of potential snow avalanche release areas or the snow avalanche high-proning areas are crucial in disaster mitigation and prevention of engineering construction in alpine mountains. This paper uses the Yining-Aksu railway in the Tianshan region of Xinjiang Uygur Autonomous Region as case study. It collectes 154 snow avalanche starting zones as assessment samples,and conductes snow avalanche susceptibility assessment based on machine learning algorithm,to construct the assessment system along the railway. We identify the potential snow avalanche release areas(PRA)based on data superposition,to map the distribution illustration of its PRA. Then we test and compare both results using Kappa coefficient and AUC values. The results show that the Kappa coefficients by SVM,MLP and PRA are 0.806,0.774 and 0.600,respectively,while AUC values are 0.993,0.961 and 0.802,respectively. Machine learning algorithms in snow avalanche susceptibility assessment outperform traditional PRA identification algorithm based on data superposition. Both machine learning algorithm models have high accuracy,with SVM performing the best,outperforming MLP. The assessment results are more consistent with the actual situation of snow avalanche development in the study area,and it can provide a basic scientific basis for snow avalanche prevention and mitigation in major engineering construction in alpine mountains. Albeit the automatic algorithm of PRA identification is relatively defective,the assessment results are basically consistent with the actual situation of snow avalanche development in the study area,making it meaningful for assessing in alpine mountain where available data is limited.
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图 2 雪崩野外调查照片及遥感解译特征
a. 铁力买提达坂残留的雪崩堆积体；b. 六月残留的雪崩堆积体热融溶洞；c. 雪崩修剪形成的旗形树；d. 拉尔墩雪崩铲刮斜坡面浮土形成的堆积体
Figure 2. Snow avalanche accumulation fieldwork photos and remote sensing interpretation features:(a) Residual snow avalanche accumulation in Tielimaiti hilltop; (b) Residual snow avalanche accumulation's hot melting cave in June; (c) Snow avalanche pruning to form a flag tree; (d) Snow avalanche scraping the slope surface to form the accumulation in Laerdun
图 3 研究区雪崩分布图
a. 铁路沿线雪崩空间分布图；b. 巩乃斯、拉尔敦段雪崩频发区域；c. 拉尔敦典型雪崩流通沟槽、雪崩堆积体；d. 阿尔先段雪崩频发区域；e. 苏力间、铁力买提达坂段雪崩频发区域
Figure 3. Snow avalanches inventory illustration of the study area:(a) Snow avalanches spatial distribution illustration along the railway; (b) Frequent snow avalanches spatial distribution illustration in Gongnaisi and Laerdun; (c) Typical snow avalanche gully and accumulation in Laerdun; (d) Frequent snow avalanches spatial distribution illustration in Aerxian; (e) Frequent snow avalanches spatial distribution illustration in Sulijian and Tielimaiti hilltop
图 5 铁路沿线雪崩易发性区划图
Figure 5. Snow avalanche susceptibility zoning illustration along the railway
图 6 雪崩形成区参数值域分布图
Figure 6. Snow avalanche starting zone parameter value range distribution chart
图 7 雪崩潜在释放区分布图
a. 铁路沿线雪崩潜在释放区空间分布图；b. 巩乃斯、拉尔敦段雪崩频发区域潜在释放区空间分布图；c. 阿尔先段雪崩频发区域潜在释放区空间分布图；d. 苏力间、铁力买提达坂段雪崩频发区域潜在释放区空间分布图
Figure 7. Potential snow avalanche release areas distribution illustration:(a) PRA spatial distribution illustration along the railway; (b) Frequent PRA spatial distribution illustration in Gongnaisi and Laerdun; (c) Frequent PRA spatial distribution illustration in Aerxian; (d) Frequent PRA spatial distribution illustration in Sulijian and Tielimaiti hilltop
图 9 铁路沿线雪崩易发性分区面积占比统计
Figure 9. Snow avalanche susceptibility zoning area statistics along the railway
表 1 雪崩形成区参数指征
Table 1. Snow avalanche starting zone parameter indications
参数 最小值 最大值 第一四分位数 第三四分位数 均值 坡度 10.00 75.10 26.13 37.21 31.46 粗糙度 1.00 3.89 1.11 1.26 1.20 曲率 0.03 60.64 8.37 19.90 14.97 
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表 2 混淆矩阵及Kappa系数
Table 2. Confusion matrix and Kappa coefficients
实际雪崩情况 预测结果 标记符号 PRA SVM MLP 1 1 N1 100 28 29 1 0 N2 8 3 5 0 1 N3 54 3 2 0 0 N4 146 28 26 Pe 0.500 0.500 0.500 Pa 0.800 0.903 0.887 Ka 0.600 0.806 0.774
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