Snow avalanche susceptibility evaluation of the Kezhayi to Gongnaisi section of the Duku expressway based on machine learning
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摘要: 独库高速公路克扎依至巩乃斯段以高山地貌为主，地形切割剧烈为雪崩发育提供了有利的地形条件，对该区域进行雪崩易发性评价是独库高速公路安全建设及运行的重要前提。通过遥感解译和现场调查等手段获取149个雪崩点的因子数据，通过对因子进行相关性检测，筛选出10个评价因子，构成雪崩评价因子体系。在此基础上，运用K均值聚类法和随机法提取出非雪崩点和原始雪崩点构成样本集，通过机器学习中的多层感知器（MLP）、支持向量机（SVM）算法对研究区域开展雪崩易发性评价。研究结果表明，随机法和K均值聚类法提取出的样本集分别带入算法中训练，R-SVM，R-MLP，K-SVM，K-MLP四种模型的Kappa系数均大于0.6，4组模型对验证数据集的预测结果与实际值存在高度的一致性。经多层感知器（MLP）训练的AUC值由0.762提高至0.983，经支持向量机（SVM）训练的AUC值由0.724提高至0.951。基于本研究预测性能最佳的K-MLP模型分区显示该研究区雪崩发育对拟建线路影响较小，但对于隧道洞口可能会造成威胁，本研究可为独库高速公路建设、运营以及雪崩灾害防治工作提供理论支撑和方法参考。Abstract: The Kezhayi to Gongnaisi section of the Duku expressway is predominantly characterized by alpine landforms, with steep terrain cutting that provides conducive conditions for sonw avalanche development. The study on the evaluation of snow avalanche susceptibility in this area is a crucial prerequisite for the safety construction and operation of the Duku expressway. The 149 snow avalanche points were collected by employing remote sensing interpretation and field investigations. Through correlation analysis of these factors, 10 evaluation factors were selected, forming the avalanche evaluation factor system. Subsequently, the non-avalanche points and original avalanche points were extracted using the K-means clustering method and random method to create a sample set. Machine learning techniques, including Multi-Layer Perceptron (MLP) and support vector machine (SVM) algorithms, were utilized to assess avalanche susceptibility in the study area. The results show that the sample datasets extracted by the random and K-means clustering methods were used for training, the Kappa coefficient of the R-SVM, R-MLP, K-SVM, and K-MLP models were greater than 0.6. These four sets of models exhibited a high degree of consistency between the predicted results and actual values of the validation dataset. The AUC (area under curve) value trained by MLP increased from 0.762 to 0.983, while the AUC value trained by SVM increased from 0.724 to 0.951. Based on the K-MLP model partition with the highest evaluation accuracy, the snow avalanche development in the research area has a relatively minor impact on the proposed route but may pose a threat to tunnel entrances. This study provides theoretical support and methodological references for the construction, operation and mitigation of sonw avalanche disasters for the Duku expressway.
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Key words:
- sonw avalanche /
- susceptibility evaluation /
- support vector machine /
- multilayer perceptron /
- ArcGIS
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图 1 研究区域雪崩遥感解译与分布图
Figure 1. Remote Sensing Interpretation and Distribution of Snow Avalanches in the Study Area
图 9 基于K-MLP模型雪崩易发性分区图
Figure 9. Snow avalanche susceptibility partition map based on K-MLP model
表 1 评价因子数据源
Table 1. Data sources for evaluates factors
分类 评价因子 数据源 地形条件 高程、坡度、坡向、地表粗糙度、
地表起伏度、高程变异系数地理空间数据云DEM 气候条件 一月平均温度、最大风速、
年平均降雨量研究区及周边各站点
的气象数据积雪条件 年平均降雪量、
最大积雪厚度研究区及周边各站点
的气象数据
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表 2 雪崩评价因子分级量化结果
Table 2. Quantitative results of snow avalanche evaluation factor grading
评价因子 二级属性 $ {S _{ij}} $ $ {N_{ij}} $ $ {X_{ij}} $ $ {C_{ij}} $ 最大风速
/m/s9～12 32,130 18 1.170 0.760 12～15 169,196 63 0.777 0.000 15～19 109,751 68 1.294 1.000 1月平均气温
/（ °C）−14～−11 39,756 15 0.788 0.045 −11～−9 201,195 71 0.737 0.000 −9～−7 70,126 63 1.876 1.000 最大积雪深度
/mm55～61 121,679 51 0.875 0.628 61～68 153,748 95 1.290 1.000 68～78 35,650 3 0.176 0.000 年平均降雨量
/mm43～45 65,586 27 0.859 0.319 45～47 127,682 31 0.507 0.000 47～49 117,809 91 1.613 1.000 年平均降雪量
/mm11～15 22,538 7 0.648 0.000 15～18 219,917 77 0.731 0.062 18～21 68,622 65 1.978 1.000 高程
/m1,873～2,295 43,244 5 0.241 0.000 2,295～2,619 82,136 15 0.381 0.051 2,619～2,927 59,328 30 1.056 0.297 2,927～3,262 51,439 31 1.258 0.370 3,262～3,627 43,467 23 1.105 0.315 3,627～4,459 31,463 45 2.986 1.000 坡度
/（°）0～10 62,301 5 0.167 0.000 10～19 56,267 18 0.668 0.253 19～28 59,776 28 0.978 0.410 28～37 62,891 40 1.328 0.587 37～47 49,479 38 1.603 0.726 47～82 19,463 20 2.145 1.000 坡向 北 67,060 31 0.962 0.373 东北 23,094 7 0.633 0.000 东 22,559 15 1.388 0.855 东南 38,180 21 1.148 0.583 南 48,476 21 0.904 0.307 西南 43,794 17 0.810 0.201 西 31,664 23 1.517 1.000 西北 35,250 14 0.829 0.222 地形起伏度
/m0～194 49,218 1 0.042 0.000 194～332 55,415 11 0.414 0.213 332～457 68,499 31 0.945 0.516 457～588 67,127 48 1.493 0.829 588～754 53,605 46 1.792 1.000 754～1263 17,213 12 1.455 0.808 地面粗糙度 1～1.1 149,984 37 0.514 0.228 1.1～1.2 83,112 48 1.206 0.536 1.2～1.4 51,767 38 1.533 0.681 1.4～1.7 19,965 21 2.196 0.976 1.7～2.4 4,640 5 2.250 1.000 2.4～7.2 709 0 0.000 0.000 地表切割度
/m0～88 74,711 2 0.056 0.000 88～161 70,384 18 0.534 0.210 161～232 62,245 23 0.771 0.315 232～309 55,587 62 2.329 1.000 309～401 39,623 36 1.897 0.810 401～682 8,527 8 1.959 0.837 高程变异系数 0～0.016 41,667 4 0.200 0.000 0.016～0.028 72,137 27 0.781 0.540 0.028～0.040 80,064 48 1.252 0.977 0.040～0.051 63,506 38 1.249 0.974 0.051～0.065 45,784 28 1.277 1.000 0.065～0.107 7,919 4 1.055 0.794
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表 3 雪崩评价因子相关性矩阵
Table 3. Correlation matrix of snow avalanche evaluation factors
最大
风速一月平均
气温年平均
降雨量年平均
降雪量最大积雪
深度高程变异
系数地形
粗糙度地表
切割度地形
起伏度坡向 坡度 高程 最大风速 1.00 一月平均气温 0.61 1.00 年平均降雨量 0.07 0.16 1.00 年平均降雪量 0.08 0.23 0.23 1.00 最大积雪深度 0.64 0.14 0.18 −0.17 1.00 高程变异系数 −0.02 0.15 0.04 0.19 −0.13 1.00 地形粗糙度 −0.11 0.06 −0.01 0.14 −0.19 0.25 1.00 地表切割度 −0.13 0.13 0.06 0.24 −0.22 0.11 0.28 1.00 地形起伏度 −0.17 0.12 0.04 0.25 −0.27 0.92 0.47 0.93 1.00 坡向 −0.01 0.02 0.00 0.02 −0.02 0.03 0.01 0.05 0.05 1.00 坡度 −0.11 0.12 0.07 0.22 −0.19 0.28 0.26 0.11 0.60 0.01 1.00 高程 −0.51 0.03 0.01 0.28 −0.54 0.17 0.34 0.21 0.44 0.03 0.12 1.00
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表 4 K均值聚类法分析结果
Table 4. Results of K-means clustering algorithm method
聚类结果 栅格数量/个 雪崩个数/个 相对雪崩比 1 13484 46 7.122 2 85561 5 0.122 3 64712 47 1.516 4 33041 7 0.442 5 114280 44 0.804
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表 5 基于K-MLP模型雪崩易发性分区结果统计
Table 5. Statistical results of snow avalanche susceptibility partition based on K-MLP model
易发性等级 栅格
数量/个面积
/km雪崩
数/个分区比例 雪崩比 雪崩密度
/（个/km）2低易发区 79284 71.35 9 25.59％ 0.06 0.12 中易发区 86287 77.66 24 27.74％ 0.16 0.31 高易发区 83594 75.23 51 26.87％ 0.34 0.68 极高易发区 61912 55.72 64 19.90％ 0.44 1.17
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