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  1. Emergency managers have the important responsibility of planning and implementing mitigation policies and programs to reduce losses to life and property. To accomplish these goals, they must use limited time and resources to ensure the communities they serve have adequately mitigated against potential disasters. As a result, it is common to collaborate and coordinate with a wide variety of partner agencies and community organizations. While it is well established that strengthening relationships and increasing familiarity improve coordination, this article advances that narrative by providing direct insights on the ways a select group of local, state, and federal emergency managers view relationships with other mitigation stakeholders. Using insights from a 1-day workshop hosted at the University of Delaware to gather information from mitigation stakeholders, this article provides a discussion of commonalities and challenges workshop participants identified with other stakeholder groups. These insights can inform other emergency managers about potential collaborators and coordination opportunities with similar stakeholders in their own communities. 
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    Free, publicly-accessible full text available May 16, 2024
  2. Abstract. Regional hurricane risk is often assessed assuming a static housing inventory, yet a region's housing inventory changes continually. Failing to include changes in the built environment in hurricane risk modeling can substantially underestimate expected losses. This study uses publicly available data and a long short-term memory (LSTM) neural network model to forecast the annual number of housing units for each of 1000 individual counties in the southeastern United States over the next 20 years. When evaluated using testing data, the estimated number of housing units was almost always (97.3 % of the time), no more than 1 percentage point different than the observed number, predictive errors that are acceptable for most practical purposes. Comparisons suggest the LSTM outperforms the autoregressive integrated moving average (ARIMA) and simpler linear trend models. The housing unit projections can help facilitate a quantification of changes in future expected losses and other impacts caused by hurricanes. For example, this study finds that if a hurricane with characteristics similar to Hurricane Harvey were to impact southeastern Texas in 20 years, the residential property and flood losses would be nearly USD 4 billion (38 %) greater due to the expected increase of 1.3 million new housing units (41 %) in the region. 
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  3. Abstract

    We develop a computational framework for the stochastic and dynamic modeling of regional natural catastrophe losses with an insurance industry to support government decision‐making for hurricane risk management. The analysis captures the temporal changes in the building inventory due to the acquisition (buyouts) of high‐risk properties and the vulnerability of the building stock due to retrofit mitigation decisions. The system is comprised of a set of interacting models to (1) simulate hazard events; (2) estimate regional hurricane‐induced losses from each hazard event based on an evolving building inventory; (3) capture acquisition offer acceptance, retrofit implementation, and insurance purchase behaviors of homeowners; and (4) represent an insurance market sensitive to demand with strategically interrelated primary insurers. This framework is linked to a simulation‐optimization model to optimize decision‐making by a government entity whose objective is to minimize region‐wide hurricane losses. We examine the effect of different policies on homeowner mitigation, insurance take‐up rate, insurer profit, and solvency in a case study using data for eastern North Carolina. Our findings indicate that an approach that coordinates insurance, retrofits, and acquisition of high‐risk properties effectively reduces total (uninsured and insured) losses.

     
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