Browsing by Author "Trainor, Joseph E."
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Item Critical Issues in Disaster Science and Management: A Dialogue Between Researchers and Practitioners(FEMA Higher Education Project, 2014) Trainor, Joseph E.; Subbio, TonyThe FEMA Higher Education Program has released a new book that combines the knowledge and experience of emergency management practitioners and researchers, the result of which is a dialogue between the two sectors about the top issues in emergency management. Critical Issues in Disaster Science and Management: A Dialogue Between Researchers and Practitioners includes 12 sections written from the views of more than 20 emergency management practitioners and researchers. The 12 sections are dialogues on: Whole community — state, local and federal relationships; Volunteers and nonprofits in disaster; Public-private partnerships; Access and functional needs; Public health preparedness; Planning and improvisation; Reflections on the National Incident Management System; Long-term recovery; After-action reporting for exercises and incidents; Social media; Professionalization of emergency management; Unmet needs and persistent problems. Co-editors, Joe Trainor and Tony Subbio, received from their queries 150 responses from emergency management practitioners and researchers about what the respondents thought were the most important topics. Trainor and Subbio spent a couple of full days narrowing down the topics to the dozen above and matched researchers/academicians with practitioners to author each section.Item Regional county-level housing inventory predictions and the effects on hurricane risk(Natural Hazards and Earth System Sciences, 2022-03-30) Williams, Caroline J.; Davidson, Rachel A.; Nozick, Linda K.; Trainor, Joseph E.; Millea, Meghan; Kruse, Jamie L.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.