More Like this

1. A stochastic optimization model for patient evacuation from health care facilities during hurricanes

   https://doi.org/10.1016/j.ijdrr.2024.104518  

   Kim, Kyoung Yoon; Toplu-Tutay, Gizem; Kutanoglu, Erhan; Hasenbein, John J (June 2024, International Journal of Disaster Risk Reduction)

   We propose a rigorous modeling and methodological effort that integrates statistical implementation of hydrology models in predicting inland and coastal flood scenarios due to hurricanes and a scenario-based stochastic integer programming model which suggests resource and staging area decisions in the first stage and the evacuation decisions in the second stage. This novel study combines physics-based flood prediction models and stochastic optimization for large- scale multi-facility coordination of hospital and nursing home evacuations before impending hurricanes. The optimization model considers scenario-dependent evacuation demand, transport vehicles with varying capacities, and both critical and non-critical patients. Utilizing Hurricane Harvey of 2017 as a case study and actual healthcare facility locations in southeast Texas, we explore various evacuation policies, demonstrating the impact of routing strategies, staging area decisions, flood thresholds, and receiving facility capacities on evacuation outcomes. One of the findings is that choosing staging area(s) and deploying evacuation vehicles optimally considering the uncertainty of the hurricane’s path at the time of decision making could have significant effect on the total cost of the operation and evacuation time experienced by the evacuees. We also show the non-negligible value of the scenario-based staging and routing solution conservatively calculated in relation to a single scenario solution using the concept of value of stochastic solution. 

   more » « less
2. A Large-Scale Patient Evacuation Modeling Framework using Scenario Generation and Stochastic Optimization

   Kim, Kyoung Yoon; Kutanoglu, Erhan; Hasenbein, John J; Wu, Wen-Ying; Yang, Zong-Liang (January 2020, IISE Annual Conference)

   null (Ed.)

   This paper proposes a two-stage stochastic mixed integer programming framework for patient evacuation. While minimizing the expected total cost of patient evacuation operations, the model determines the location of staging areas and the number of emergency medical service (EMS) vehicles to mobilize in the first stage, and the EMS vehicle routing assignments in the second stage. A real-world data from Southeast Texas region is used to demonstrate our modeling approach. To provide a more pragmatic solution to the patient evacuation problem, we attempt to create comprehensive hurricane instances by integrating the publicly available state-of-art hydrology models for surge, Sea, Lake Ocean and Overland Surge for Hurricanes (SLOSH), and for streamflow, National Water Model (NWM), prediction. The surge product captures potential flooding in coastal region while the streamflow product predicts inland flooding. The results exhibit the importance of the integrated approach in patient evacuation planning, provide guidance on flood mapping and prove the potential benefit of comprehensive approach in scenario generation. 

   more » « less
3. Hurricane Scenario Generation for Uncertainty Modeling of Coastal and Inland Flooding

   https://doi.org/10.3389/fclim.2021.610680  

   Kim, Kyoung Yoon; Wu, Wen-Ying; Kutanoglu, Erhan; Hasenbein, John J.; Yang, Zong-Liang (March 2021, Frontiers in Climate)

   null (Ed.)

   Hurricanes often induce catastrophic flooding due to both storm surge near the coast, and pluvial and fluvial flooding further inland. In an effort to contribute to uncertainty quantification of impending flood events, we propose a probabilistic scenario generation scheme for hurricane flooding using state-of-art hydrological models to forecast both inland and coastal flooding. The hurricane scenario generation scheme incorporates locational uncertainty in hurricane landfall locations. For an impending hurricane, we develop a method to generate multiple scenarios by the predicated landfall location and adjusting corresponding meteorological characteristics such as precipitation. By combining inland and coastal flooding models, we seek to provide a comprehensive understanding of potential flood scenarios for an impending hurricane. To demonstrate the modeling approach, we use real-world data from the Southeast Texas region in our case study. 

   more » « less
4. Evacuate or social distance? Modeling the influence of threat perceptions on hurricane evacuation in a dual‐threat environment

   https://doi.org/10.1111/risa.14202  

   Wu, Hao‐Che; Murphy, Haley; Greer, Alex; Clay, Lauren (August 2023, Risk Analysis)

   Abstract This study investigates how different risk predictors influenced households’ evacuation decisions during a dual‐threat event (Hurricane Laura and COVID‐19 pandemic). The Protective Action Decision Model (PADM) literature indicates that perceived threat variables are the most influential variables that drive evacuation decisions. This study applies the PADM to investigate a dual‐threat disaster that has conflicting protective action recommendations. Given the novelty, scale, span, impact, and messaging around COVID‐19, it is crucial to see how hurricanes along the Gulf Coast—a hazard addressed seasonally by residents with mostly consistent protective action messaging—produce different reactions in residents in this pandemic context. Household survey data were collected during early 2021 using a disproportionate stratified sampling procedure to include households located in mandatory and voluntary evacuation areas across the coastal counties in Texas and parishes in Louisiana that were affected by Hurricane Laura. Structural equation modeling was used to identify the relationships between perceived threats and evacuation decisions. The findings suggest affective risk perceptions strongly affected cognitive risk perceptions (CRPs). Notably, hurricane and COVID‐19 CRPs are significant predictors of hurricane evacuation decisions in different ways. Hurricane CRPs encourage evacuation, but COVID‐19 CRPs hinder evacuation decisions. 

   more » « less
5. Effects of the Uncertainty of Hurricane Tracks on Coastal Hazards and Evacuations

   Yang, J. (November 2022, International journal of structural and civil engineering research)

   Hurricanes cause devastating amounts of damage to structures and infrastructure. It harms especially those coastal residents along its track. Over the last couple of years, evacuation planning for populated coastal regions has been challenging and time-consuming due to the uncertainty of the hurricane’s track. As such, with a focus on Northwest Florida, this research aims to focus on the development of evacuation scenarios for coastal communities that combines hurricane inundation and strong wind forecast and evacuation modeling. The proposed approach integrates storm surge simulation models (ADCIRC and SWAN modeling) and traffic evacuation models (Cube and TIME) by using hurricane forecasting datasets to explore the designation of evacuation zones and the calculation of evacuation clearance times in different counties. This approach was applied to three distinct scenarios with a focus on possible populated coastal cities that Hurricane Michael would have hit in 2018. Selected cities are Pensacola, Destin, and Panama City. This type of approach has the potential to help agencies make more informed decisions on evacuations using the accuracy and timeliness of forecasts and provide safer evacuations in coastal areas by avoiding the traffic jams on evacuation routes. 

   more » « less