基于加权证据权模型的青藏高原东部巴塘断裂带滑坡易发性评价

巴塘断裂带位于青藏高原东部,呈北东—南西向展布,全新世活动强烈,沿断裂带崩塌、滑坡、泥石流等地质灾害极为发育。基于遥感解译和野外地质调查,在巴塘断裂带两侧10 km范围内识别出滑坡93处;在分析滑坡空间发育特征的基础上,选取地形地貌(地面高程、地形坡度和地形坡向)、地形湿度指数、地层岩性、活动断裂、降雨量、水系、人类工程活动和植被覆盖等10个因素作为滑坡易发程度的主控因素,采用加权证据权法建立滑坡易发性评价模型,开展巴塘断裂带滑坡易发性评价;成功率(ROC)曲线检验结果表明此次滑坡易发性评价的准确率为82.3%。采用基于自然断点法将滑坡易发程度划分为极高易发、高易发、中等易发和低易发4个级别,结果表明滑坡易发性受巴塘断裂带和河流控制显著,极高易发区和高易发区主要分布在巴塘断裂带、金沙江和巴曲河谷及一级支流两侧,中等易发区主要分布在巴曲各支流中上游,低易发区主要分布在人类工程活动弱的高山地带以及地形相对平缓的区域。滑坡易发性评价结果很好地反映了巴塘断裂带现今滑坡发育分布特征,对该区重大工程规划建设和防灾减灾具有科学指导意义。     

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

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).     

川藏铁路雅安至昌都段风吹雪空间分异特征及易发性评价

川藏铁路大段区域位于青藏高原地区,冬季气候寒冷,降雪量大,在当前气候变化背景下面临严峻的风吹雪问题.本文先通过层次分析法构建了川藏铁路沿线风吹雪灾害区域易发性评价体系,然后以重点工段——毛垭草原明线段为例,针对工点开展风吹雪发生危险度和到达危险度评价,从面到点,层层递进地开展风险评价.结果显示铁路沿线风吹雪易发性指数高的区域主要分布在新都桥、理塘、贡觉等地,重点工段毛垭草原明线段存在风吹雪次危险.通过数值模拟对毛垭草原明线段开展风吹雪到达危险度评价,结果显示积雪严重区域主要在风速减弱区,集中在两侧坡面防护的坡脚处以及路堤和防护之间的“凹陷”地带即排水沟;在桥梁工段,气流减速区域主要出现在桥梁的背风侧.所提出的风吹雪区域易发性评价体系和针对到工点的风险评价方法可为川藏铁路后续明线工程的设计和施工提供一定参考.