| 摘要: |
| 河道堰塞是指由于山体滑坡、泥石流等地质灾害产生并运移的固体物质堵塞河流，导致河道堵塞的现象。河道堰塞后形成的堰塞湖是一种高风险高危害的自然灾害，会造成上游雍水，淹没土地，改变生态系统结构等严重损失。在河道堰塞灾害造成的众多损失中，风险人口的生命损失是最难以挽回的损失。本文根据河道堰塞生命损失的致灾机理构建了风险人口、警报时间、理解程度、湖区水位上升速度、风险人口高程、淹没水深等参数在内的评价指标体系，并确立了各参数的定量评估方法。同时通过各因素间的作用关系建立了由淹没水深网络、应急疏散网络、生命损失定量评估网络三个子网络系统组成的贝叶斯网络模型对河道堰塞生命损失进行评估，并采用牛栏江河道堰塞案例验证了评估方法的合理性。 |
| 关键词: 河道堰塞 生命损失 评估 贝叶斯网络 牛栏江 |
| DOI： |
| 分类号:TV698.2??????????????????????????????????????????????????????????????????? |
| 基金项目:国家重点研发计划课题(2018YFC1508604)；中央级公益性科研院所基本科研业务费专项资金(Y319003,Y320005) |
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| Study on the evaluation method of loss of life due to river clogging |
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Huliang1, Qianyajun2, Fuzhongzhi2, Weiyinqi3, Zhongqiming2
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1.Hohai University;2.Nanjing Hydraulic Research Institute;3.China Institute of Water Resources and Hydropower Research
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| Abstract: |
| River clogging refers to the phenomenon that the river is blocked by the solid material which is generated and transported by geological disasters such as landslides and debris flows. The dammed lake formed after river clogging is a kind of natural disaster with high risk and hazard, which will cause serious losses of damming, inundation of land and change of ecosystem structure. Among the numerous losses caused by the river clogging, the loss of the lives of the people at risk is the most irreparable loss. Based on the disaster-causing mechanism of loss of life due to river clogging, the evaluation index system of risk population, warning time, understanding degree, rising speed of water level, elevation of risk people, submerged water depth is constructed, and the quantitative evaluation method of each parameter is established. Simultaneously, a Bayesian network model composed of three sub-network systems, namely network of submerged water depth, network of emergency evacuation, and network of quantitative evaluation of loss of life, is developed to evaluate the loss of life due to river clogging. Further, the river clogging case of Niulan River is used to testify to the rationality of the evaluation method. |
| Key words: River clogging loss of life evaluation Bayesian network Niulan River |
