Closing the Gap from Uncertainty Quantification to Decision Making:
Integrated Prediction-Optimization Modeling of the Critical
Infrastructure Flood Resilience
Abstract
Our research team is involved in several projects that seek to integrate
the science-based prediction models of flood-causing events such as
hurricanes with the decision-making models for critical infrastructure
resilience. To this end, we use the state-of-the-art hydrological models
such as WRF-Hydro and ADCIRC to simulate potential realizations of
inland and coastal flooding events caused by tropical storms. We use
these simulations to generate statistically sound scenarios to populate
the inputs of several resilience-based decision making models, all
developed using the state-of-the-art scenario-based stochastic and
robust optimization methodologies. We identify three time lines where
these models can be used to improve the quality of decision making
processes: (1) Short-term preemptive resource allocation (preparedness)
just before impending tropical storms, (2) Mid-term hardening and
resilience investment strategies (mitigation) within a multi-season
horizon considering multitudes of potential storms, and (3) Long-term
resilience investment and infrastructure design strategy development
considering potentially increasing flooding risks due to climate change
and sea level rise. We present the overall framework that our team
developed relying on the team’s in-progress work, particularly for the
short- and mid-term prediction-optimization models. We use two specific
infrastructures as examples to instantiate our models: (1) Evacuation of
patients from healthcare facilities (hospitals and nursing homes), and
(2) Substation hardening and preparation for power grids. To create
realistic, high-resolution case studies, we consider historical and
synthetic storms that impact actual healthcare facilities and power grid
for Texas.