期刊
NATURAL HAZARDS AND EARTH SYSTEM SCIENCES
卷 22, 期 3, 页码 1055-1072出版社
COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/nhess-22-1055-2022
关键词
-
资金
- National Science Foundation [1830511]
- Directorate For Engineering
- Div Of Civil, Mechanical, & Manufact Inn [1830511] Funding Source: National Science Foundation
This study uses an LSTM neural network model to forecast the number of housing units in the southeastern United States for the next 20 years, and the results show that the model performs well in predicting. The LSTM outperforms other models. These projections of housing units can help evaluate changes in losses and other impacts caused by hurricanes.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据