独库高速公路克扎依—巩乃斯段雪崩易发性评价
P642.21; 独库高速公路克扎依—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、支持向量机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,...
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| Published in | 中国地质灾害与防治学报 Vol. 35; no. 1; pp. 60 - 71 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
新疆农业大学交通与物流工程学院,新疆乌鲁木齐 830052
2024
新疆交通规划勘察设计研究院有限公司/科技研发中心,新疆乌鲁木齐 830006%新疆交通规划勘察设计研究院有限公司/科技研发中心,新疆乌鲁木齐 830006 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1003-8035 |
| DOI | 10.16031/j.cnki.issn.1003-8035.202302009 |
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| Abstract | P642.21; 独库高速公路克扎依—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、支持向量机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.经多层感知器训练的AUC值由0.762提高至0.983,经支持向量机训练的AUC值由0.724提高至0.95 1.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. |
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| AbstractList | P642.21; 独库高速公路克扎依—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、支持向量机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.经多层感知器训练的AUC值由0.762提高至0.983,经支持向量机训练的AUC值由0.724提高至0.95 1.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_FL | The Kezhayi to Gongnaisi section of the Duku expressway is predominantly characterized by alpine landforms,with steep terrain cutting that provides conducive conditions for 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 multilayer 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. |
| Author | 杨治纬 刘杰 张天意 程秋连 王斌 |
| AuthorAffiliation | 新疆农业大学交通与物流工程学院,新疆乌鲁木齐 830052;新疆交通规划勘察设计研究院有限公司/科技研发中心,新疆乌鲁木齐 830006%新疆交通规划勘察设计研究院有限公司/科技研发中心,新疆乌鲁木齐 830006 |
| AuthorAffiliation_xml | – name: 新疆农业大学交通与物流工程学院,新疆乌鲁木齐 830052;新疆交通规划勘察设计研究院有限公司/科技研发中心,新疆乌鲁木齐 830006%新疆交通规划勘察设计研究院有限公司/科技研发中心,新疆乌鲁木齐 830006 |
| Author_FL | ZHANG Tianyi YANG Zhiwei LIU Jie WANG Bin CHENG Qiulian |
| Author_FL_xml | – sequence: 1 fullname: CHENG Qiulian – sequence: 2 fullname: LIU Jie – sequence: 3 fullname: YANG Zhiwei – sequence: 4 fullname: ZHANG Tianyi – sequence: 5 fullname: WANG Bin |
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| Keywords | ArcGIS 易发性评价 支持向量机 support vector machine susceptibility evaluation multilayer perceptron avalanche 雪崩 多层感知器 |
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| Publisher | 新疆农业大学交通与物流工程学院,新疆乌鲁木齐 830052 新疆交通规划勘察设计研究院有限公司/科技研发中心,新疆乌鲁木齐 830006%新疆交通规划勘察设计研究院有限公司/科技研发中心,新疆乌鲁木齐 830006 |
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| Title | 独库高速公路克扎依—巩乃斯段雪崩易发性评价 |
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