Risk-driven statistical modeling for hurricane-induced compound events: Design event implementation for industrial areas subjected to coastal floods and winds

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.