An Integrated Scenario Ensemble-Based Framework for Hurricane Evacuation Modeling: Part 1-Decision Support System.

This article introduces a new integrated scenario-based evacuation (ISE) framework to support hurricane evacuation decision making. It explicitly captures the dynamics, uncertainty, and human-natural system interactions that are fundamental to the challenge of hurricane evacuation, but have not been fully captured in previous formal evacuation models. The hazard is represented with an ensemble of probabilistic scenarios, population behavior with a dynamic decision model, and traffic with a dynamic user equilibrium model. The components are integrated in a multistage stochastic programming model that minimizes risk and travel times to provide a tree of evacuation order recommendations and an evaluation of the risk and travel time performance for that solution. The ISE framework recommendations offer an advance in the state of the art because they: (1) are based on an integrated hazard assessment (designed to ultimately include inland flooding), (2) explicitly balance the sometimes competing objectives of minimizing risk and minimizing travel time, (3) offer a well-hedged solution that is robust under the range of ways the hurricane might evolve, and (4) leverage the substantial value of increasing information (or decreasing degree of uncertainty) over the course of a hurricane event. A case study for Hurricane Isabel (2003) in eastern North Carolina is presented to demonstrate how the framework is applied, the type of results it can provide, and how it compares to available methods of a single scenario deterministic analysis and a two-stage stochastic program.

Statistical Modeling of Fire Occurrence Using Data from the Tohoku, Japan Earthquake and Tsunami.

In this article, we develop statistical models to predict the number and geographic distribution of fires caused by earthquake ground motion and tsunami inundation in Japan. Using new, uniquely large, and consistent data sets from the 2011 Tohoku earthquake and tsunami, we fitted three types of models-generalized linear models (GLMs), generalized additive models (GAMs), and boosted regression trees (BRTs). This is the first time the latter two have been used in this application. A simple conceptual framework guided identification of candidate covariates. Models were then compared based on their out-of-sample predictive power, goodness of fit to the data, ease of implementation, and relative importance of the framework concepts. For the ground motion data set, we recommend a Poisson GAM; for the tsunami data set, a negative binomial (NB) GLM or NB GAM. The best models generate out-of-sample predictions of the total number of ignitions in the region within one or two. Prefecture-level prediction errors average approximately three. All models demonstrate predictive power far superior to four from the literature that were also tested. A nonlinear relationship is apparent between ignitions and ground motion, so for GLMs, which assume a linear response-covariate relationship, instrumental intensity was the preferred ground motion covariate because it captures part of that nonlinearity. Measures of commercial exposure were preferred over measures of residential exposure for both ground motion and tsunami ignition models. This may vary in other regions, but nevertheless highlights the value of testing alternative measures for each concept. Models with the best predictive power included two or three covariates.

Modeling insurer-homeowner interactions in managing natural disaster risk.

The current system for managing natural disaster risk in the United States is problematic for both homeowners and insurers. Homeowners are often uninsured or underinsured against natural disaster losses, and typically do not invest in retrofits that can reduce losses. Insurers often do not want to insure against these losses, which are some of their biggest exposures and can cause an undesirably high chance of insolvency. There is a need to design an improved system that acknowledges the different perspectives of the stakeholders. In this article, we introduce a new modeling framework to help understand and manage the insurer's role in catastrophe risk management. The framework includes a new game-theoretic optimization model of insurer decisions that interacts with a utility-based homeowner decision model and is integrated with a regional catastrophe loss estimation model. Reinsurer and government roles are represented as bounds on the insurer-insured interactions. We demonstrate the model for a full-scale case study for hurricane risk to residential buildings in eastern North Carolina; present the results from the perspectives of all stakeholders-primary insurers, homeowners (insured and uninsured), and reinsurers; and examine the effect of key parameters on the results.