摘要
冰湖溃决不仅对财产和基础设施具有破坏性,而且对当地居民也构成极大威胁。冰湖溃决的预测和风险评估对于预防和减轻灾害影响至关重要。文中提出了一个冰湖溃决的预测模型,强调选取容易获得的预测因子。以喜马拉雅山地区的48个冰湖为样本,使用地理探测器检测4个选定的预测因子:母冰川面积、冰舌坡度、冰湖面积和坝顶宽度。结果显示:冰舌坡度q值最大,为0.334 2。在交互作用检测器中,母冰川面积和冰舌坡度在交互作用后有最高的解释力,为0.684 4。这表明:与冰湖和冰碛坝相比,母冰川对冰湖状态的影响更大。在利用SVM(Support Vector Machine,支持向量机)构建的冰湖溃决预测模型中,验证集和测试集的准确率分别为83.33%和87.5%。研究为喜马拉雅地区未来的灾害管理提供了相应参考。
Glacial lake outburst floods(GLOFs)are destructive not only to the property and infrastructure but also to people living in the regions. GLOFs prediction and risk evaluation are critical for preventing and mitigating the negative impacts. This paper proposes a prediction model for the possibility of GLOFs,which emphasizes the selection of easily available predictors. Taking 48 glacial lakes in the Himalayas as samples,the geodetector is used to detect4 selected predictors:the area of mother glacier,the slope of glacier tongue,the area of glacial lake and the width of dam crest. The result shows the slope of glacier tongue has the largest q value of 0.334 2. In the interaction detector,the area of mother glacier and the slope of glacier tongue had the highest explanatory power of 0.684 4 after the interaction. It reveals that the mother glacier has a greater influence on the glacial lake state,compared to the glacial lake and the moraine dam. In the GLOFs prediction model based on SVM,the accuracy of the verification set and test set were 83.33% and 87.5%,respectively. The results can provide practical and efficient reference for local government and people. This study provides a suitable context for the future disaster management and risk reduction policy framing in Himalayas.
作者
汪宙峰
贺相綦
王成武
WANG Zhoufeng;HE Xiangqi;WANG Chengwu(School of Geoscience and Technology,Southwest Petroleum University,Chengdu 610500,China;State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation,Southwest Petroleum University,Chengdu 610500,China;Natural Gas Geology Key Laboratory of Sichuan Province,Southwest Petroleum University,Chengdu 610500,China)
出处
《自然灾害学报》
CSCD
北大核心
2022年第6期220-228,共9页
Journal of Natural Disasters
基金
国家重点研发计划(2020YFF0414359)
重庆市科技局技术创新与应用发展专项(CSTC219JSCX-msxmX0311)
成都市科技人才创新创业项目(2021-RC03-00027-CG)
四川省重点研发计划(2023YFS0406)。
