Modelling multi-hazard hurricane damages on an urbanized coast with a Bayesian Network approach
Modelling multi-hazard hurricane damages on an urbanized coast with a Bayesian Network approach
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使用贝叶斯网络方法对城市化海岸的多灾种飓风损害进行建模
DOI：
10.1016/j.coastaleng.2015.05.006
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发表时间：
2015
影响因子：
4.4
通讯作者：
C. D. Heijer
中科院分区：
文献类型：
--
作者：
H. Verseveld;A. Dongeren;N. Plant;W. Jäger;C. D. Heijer
飓风洪水对沿海地区住宅建筑的影响是由许多危害引起的，如洪水、溢流、侵蚀和波浪袭击。然而，传统的飓风破坏模型通常使用阶段破坏函数，其中阶段仅与洪水深度相关。此外，这些模型是确定性的，没有考虑与过程本身和预测相关的大量不确定性。这种不确定性变得越来越重要，当多个危险（洪水，波浪袭击，侵蚀等）。是同时考虑的。本文的重点是建立观测到的损害和多个危险指标之间的关系，以更好地进行概率预测。该概念包括：（1）使用近岸形态动力学模型XBeach，从事后预报的风暴中确定局部灾害指标（LHI），以及（2）将这些LHI和建筑物特征与观察到的损害相结合。我们选择了贝叶斯网络方法来进行这种耦合，并使用LHI的“淹没深度”、“流速”、“波浪攻击”和“冲刷深度”来表示洪水、水流、波浪影响和侵蚀相关的灾害。耦合的灾害模型根据纽约州洛克威半岛的一个案例现场的4000个损害观测结果进行了测试，该案例在10月下旬受到飓风桑迪的影响。2012.该模型能够在95%的时间内准确区分“轻微损害”和所有其他结果，并能够在68%的时间内区分受风暴影响但未严重受损的地区。对于受损最严重的建筑物（“重大损坏”和“摧毁”），对预期损坏的预测低估了观察到的损坏。该模型表明，包括多个危险使预测技能加倍，当仅考虑一个危险时，对数似然比检验（提高准确性和减少不确定性的措施）得分在0.02和0.17之间，当同时考虑多个危险时，得分为0.37。预测能力最强的LHI是“淹没深度”和“波浪攻击”。贝叶斯网络方法比市场标准的阶段损害函数有几个优点：多个指标的预测能力可以结合起来;可以获得概率预测，其中包括不确定性;定量和描述性信息可以同时使用。
Hurricane flood impacts to residential buildings in coastal zones are caused by a number of hazards, such as inundation, overflow currents, erosion, and wave attack. However, traditional hurricane damage models typically make use of stage-damage functions, where the stage is related to flooding depth only. Moreover, these models are deterministic and do not consider the large amount of uncertainty associated with both the processes themselves and with the predictions. This uncertainty becomes increasingly important when multiple hazards (flooding, wave attack, erosion, etc.) are considered simultaneously. This paper focusses on establishing relationships between observed damage and multiple hazard indicators in order to make better probabilistic predictions. The concept consists of (1) determining Local Hazard Indicators (LHIs) from a hindcasted storm with use of a nearshore morphodynamic model, XBeach, and (2) coupling these LHIs and building characteristics to the observed damages. We chose a Bayesian Network approach in order to make this coupling and used the LHIs ‘Inundation depth’, ‘Flow velocity’, ‘Wave attack’, and ‘Scour depth’ to represent flooding, current, wave impacts, and erosion related hazards.The coupled hazard model was tested against four thousand damage observations from a case site at the Rockaway Peninsula, NY, that was impacted by Hurricane Sandy in late October, 2012. The model was able to accurately distinguish ‘Minor damage’ from all other outcomes 95% of the time and could distinguish areas that were affected by the storm, but not severely damaged, 68% of the time. For the most heavily damaged buildings (‘Major Damage’ and ‘Destroyed’), projections of the expected damage underestimated the observed damage. The model demonstrated that including multiple hazards doubled the prediction skill, with Log-Likelihood Ratio test (a measure of improved accuracy and reduction in uncertainty) scores between 0.02 and 0.17 when only one hazard is considered and a score of 0.37 when multiple hazards are considered simultaneously. The LHIs with the most predictive skill were ‘Inundation depth’ and ‘Wave attack’. The Bayesian Network approach has several advantages over the market-standard stage-damage functions: the predictive capacity of multiple indicators can be combined; probabilistic predictions can be obtained, which include uncertainty; and quantitative as well as descriptive information can be used simultaneously.
影响因子：
4.1
作者：
D. Wyncoll;B. Gouldby
通讯作者：
D. Wyncoll;B. Gouldby