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• W2894489221 abstract "Flood susceptibility in the Lower Connecticut River Valley Region attributable to nonclimatic flood risk factors is mapped using a quantitative method using logistic regression. Flood risk factors considered include elevation, slope, curvature (concave, convex, or flat), distance to water, land cover, vegetative density, surficial materials, soil drainage, and impervious surface. Values of factors at point locations were correlated to whether a location was located within or outside of the U.S. Federal Emergency Management Agency 100-year Special Flood Hazard Area (SFHA). The Lower Connecticut River Valley Region was divided into urban, rural, and coastal subregions to assess the differences in factor contributions to flood susceptibility between different region types; for each region flood risk factors were extracted from 4,000 points, of which an equal number were within or outside of the 100-year SFHA. Logistic regression coefficients were obtained. It was found that elevation and distance to water have the greatest contribution to flood susceptibility in the urban and coastal subregions, whereas distance to water and surficial materials dominate in the rural subregion. The contribution of land use to flood susceptibility increased by over 200% between the rural and urban regions. Probabilities of flooding were computed using each regional logistic regression equation. Several areas classified as very high risk (80–100%) and high risk (60–80%) were located outside of the SFHA and included several types of infrastructure critical for human health, safety, and education. This study demonstrates the utility of logistic regression as an efficient methodology to map regional flood susceptibility." @default.
• W2894489221 created "2018-10-05" @default.
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• W2894489221 date "2018-10-01" @default.
• W2894489221 modified "2023-09-27" @default.
• W2894489221 title "A Statistical Approach to Mapping Flood Susceptibility in the Lower Connecticut River Valley Region" @default.
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• W2894489221 doi "https://doi.org/10.1029/2018wr023018" @default.
• W2894489221 hasPublicationYear "2018" @default.
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