TY - JOUR
T1 - Risk-driven statistical modeling for hurricane-induced compound events
T2 - Design event implementation for industrial areas subjected to coastal floods and winds
AU - Lan, Meng
AU - Gardoni, Paolo
AU - Luo, Ruiyu
AU - Zhu, Jiping
AU - Lo, Siuming
N1 - Funding Information:
The authors would like to thank Professor Kerry Emanuel for their data support and help. We are also thankful for the support of the National Key Research and Development Plan of China (Grant No. 2020YFC1522805 , 2016YFC0800104 , 2016YFC0800601 ), the Major Program of National Natural Science Foundation of China ( 51936011) , and the Fundamental Research Funds for the Central Universities of China (Grant No. WK2320000040 ). The authors also would like to acknowledge high-performance computing support from the Texas Advanced Computing Center (TACC).
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/5/1
Y1 - 2022/5/1
N2 - Hurricane-induced compound events (HICEs), such as coastal surges and winds, usually exhibit a high degree of nonlinear dependence, and thus, a single disaster modeling method cannot effectively evaluate and design the corresponding engineering applications. Therefore, this research aims at developing a statistical model suitable for HICEs to analyze and design multivariate hazard scenarios. Simultaneously, a risk-driven weighting function is constructed, considering the likelihood of event occurrence and response of the targets, to identify the riskiest design event in the critical event set. We apply the proposed model to an industrial area on the Galveston coast; use numerically synthesized HICEs to explore the dependence of the flood height, wind speed, and current velocity; and discuss the effects of different weighting rules on the design events. The modeling results show that three marginal variables are significantly correlated with one another, and the correlation between the flood height and wind speed in extreme events is enhanced. Additionally, on the same set of critical events, the riskiest event is typically not the most likely event, and the difference between them decreases as the return period increases. Moreover, the risk-driven weighting function provides a reliable scheme for disaster prevention design events of special petrochemical facilities.
AB - Hurricane-induced compound events (HICEs), such as coastal surges and winds, usually exhibit a high degree of nonlinear dependence, and thus, a single disaster modeling method cannot effectively evaluate and design the corresponding engineering applications. Therefore, this research aims at developing a statistical model suitable for HICEs to analyze and design multivariate hazard scenarios. Simultaneously, a risk-driven weighting function is constructed, considering the likelihood of event occurrence and response of the targets, to identify the riskiest design event in the critical event set. We apply the proposed model to an industrial area on the Galveston coast; use numerically synthesized HICEs to explore the dependence of the flood height, wind speed, and current velocity; and discuss the effects of different weighting rules on the design events. The modeling results show that three marginal variables are significantly correlated with one another, and the correlation between the flood height and wind speed in extreme events is enhanced. Additionally, on the same set of critical events, the riskiest event is typically not the most likely event, and the difference between them decreases as the return period increases. Moreover, the risk-driven weighting function provides a reliable scheme for disaster prevention design events of special petrochemical facilities.
KW - Coastal flood
KW - Dependence
KW - Hurricane-induced compound events
KW - Multivariate
KW - Return period
KW - Strong wind
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U2 - 10.1016/j.oceaneng.2022.111159
DO - 10.1016/j.oceaneng.2022.111159
M3 - Article
AN - SCOPUS:85127126099
SN - 0029-8018
VL - 251
JO - Ocean Engineering
JF - Ocean Engineering
M1 - 111159
ER -