Coastal communities are susceptible to flooding due to tropical storms, hurricanes, and heavy rainfall events. These events have increased recently in frequency and intensity. Therefore, it is critical to develop smart, science-based systems, tools and models that capture the underlying behavior of coastal hazards, and to coordinate and optimize decisions before, during and after natural hazards, to enhance the resilience and response of coastal communities. This research undertakes an exploratory and unifying research agenda focused on integrating geosciences-based modeling of coastal floods with scenario-based stochastic optimization for human-centric decision-making problems that coastal communities face in the wake of hurricanes and other flood-inducing events. Using such events as archetypal coastal hazards, the project addresses a specific human-centric problem: evacuating patients from hospitals and nursing homes just before such hazards. Patient evacuation planning is especially important as mismanagement has several times led to unnecessary deaths in hospitals, in nursing homes, or during evacuation. Development of an effective decision support tool, to be used by regional evacuation coordination agencies, could have wide-ranging impact across the United States in future disasters. Indeed, a primary goal of this research is to create a tool that can be disseminated for national use. The knowledge and tools developed on large-scale multi-hospital patient evacuation will lead to new ways to optimally coordinate limited resources when faced with uncertain but predictable events such as hurricanes. Moreover, this integrated approach is extendable to other coastal logistical problems (e.g., prepositioning emergency supplies, siting shelters, prepositioning repair resources and spares for critical infrastructure recovery) thus initiating new research agendas. This research also features robust collaboration with various organizations, including those involved in weather, hurricane, and flood prediction, and emergency management and evacuation, in order to ensure feasibility and usability of the tools produced. On the educational front, the PIs will create teaching modules on evacuation modeling and develop a new course on humanitarian operations research.
This project focuses on a specific problem that significantly affects coastal communities in order to highlight the value of integrating geosciences-based modeling of coastal floods with scenario-based stochastic optimization: optimizing large-scale multi-hospital and nursing home evacuation in response to flood-inducing events. This high-stakes problem needs accurate flood predictions. In particular, this research integrates coupled weather forecast, runoff production, river routing, inundation mapping models (in general, geoscience models) with an underlying stochastic optimization model of the decision-making problem. The main use of the geoscience models will be the rigorous generation of flooding scenarios that will serve as input to the stochastic optimization models. The modular architecture of the Weather Research and Forecasting Model, hydrological modeling system (WRF-Hydro), with the Noah Land Surface model (LSM), will be coupled to a vector-based river routing model (RAPID). The integrated geoscience model will generate statistically-grounded flooding scenarios before a hurricane or heavy rainfall event in order to improve recommendations for resource allocation and logistics decisions (e.g., staging area locations, allocation of medical personnel, allocation/routing of ambulances between sending and receiving facilities, etc.). Finally, recognizing the uncertainty in the hurricane forecasts, this effort generates a series of flood scenarios (instead of a single realization) to be used in the patient evacuation problem, which was not done before. A significant merit of the proposed work is to bring together two research communities that do not usually work closely together: operations research and geosciences modeling. In creating this bridge, the research links the predictive power of geosciences modeling with the prescriptive power of stochastic optimization.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
