Warning Decision-Making for Landslide Dam Breaching Flood Using Influence Diagrams

被引:2
|
作者
Zhu, Yan [1 ,2 ,3 ]
Peng, Ming [1 ,2 ]
Zhang, Peng [4 ]
Zhang, Limin [5 ]
机构
[1] Tongji Univ, Key Lab Geotech & Underground Engn, Minist Educ, Shanghai, Peoples R China
[2] Tongji Univ, Coll Civil Engn, Dept Geotech Engn, Shanghai, Peoples R China
[3] China Shipbldg NDRI Engn Co Ltd, Shanghai Res Ctr Ocean & Shipbldg Engn, Shanghai, Peoples R China
[4] Qingdao Univ Technol, Coll Civil Engn, Qingdao, Peoples R China
[5] Hong Kong Univ Sci & Technol, Dept Civil & Environm Engn, HKSAR, Hong Kong, Peoples R China
基金
中国国家自然科学基金;
关键词
decision-making; Influence Diagrams; landslide dam; risk assessment; evacuation warning; BREAK EMERGENCY MANAGEMENT; PART; 2; APPLICATION; HUMAN RISKS; SIMULATION; MODEL;
D O I
10.3389/feart.2021.679862
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Warning and evacuation are among the most effective ways for saving human lives and properties from landslide dam hazards. A new warning decision model for landslide dam break is developed using Influence Diagrams to minimize the total losses. An Influence Diagram is a simple visual representation of a decision problem. It analyzes the qualitative (causal) relationships between the variables via a logic diagram and determines the quantitative relationships via conditional probability and Bayes' theorem. The model is applied for the warning decision-making of the 2008 Tangjiashan landslide dam. The new model unifies the dam failure probability, evacuation, life loss, and flood damage in an Influence Diagram. Besides, a warning criterion is proposed for efficient decision-making. The model is more advanced than the decision tree since the inter-relationships of influence factors are qualitatively analyzed with causality connections and quantitatively analyzed with conditional probabilities. It is more efficient than a dynamic decision-making model (DYDEM) as it can directly calculate the three types of flood loss (i.e., evacuation cost, flood damage, and monetized life loss) and the expected total loss. Moreover, the probabilities of the influence factors leading to known results can be obtained through inversion analysis based on Bayesian theory. The new warning decision model offers an efficient way to save lives from landslide dam breaking and avoid unnecessary expenses from premature warning and evacuation.
引用
收藏
页数:18
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