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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/nhess-2020-347
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2020-347
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  28 Oct 2020

28 Oct 2020

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This preprint is currently under review for the journal NHESS.

Quantification of Continuous Flood Hazard using Random Forrest Classification and Flood Insurance Claims at Large Spatial Scales: A Pilot Study in Southeast Texas

William Mobley1, Antonia Sebastian1,2, Russell Blessing1, Wesley E. Highfield1, Laura Stearns1, and Samuel D. Brody1 William Mobley et al.
  • 1Department of Marine Sciences, Texas AM University at Galveston, Galveston, Texas USA
  • 2Department of Geological Sciences, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina USA

Abstract. Pre-disaster planning and mitigation necessitates detailed spatial information about flood hazards and their associated risks. In the U.S., the FEMA Special Flood Hazard Area (SFHA) provides important information about areas subject to flooding during the 1 % riverine or coastal event. The binary nature of flood hazard maps obscures the distribution of property risk inside of the SFHA and the residual risk outside of the SFHA, which can undermine mitigation efforts. Machine-learning techniques provide an alternative approach to estimating flood hazards across large spatial scales at low computational expense. This study presents a pilot study for the Texas Gulf Coast Region using Random Forest Classification to predict flood probability across a 30,523 km2 area. Using a record of National Flood Insurance Program (NFIP) claims dating back to 1976 and high-resolution geospatial data, we generate a continuous flood hazard map for twelve USGS HUC-8 watersheds. Results indicate that the Random Forest model predicts flooding with a high sensitivity (AUC 0.895), especially compared to the existing FEMA regulatory floodplain. Our model identifies 649,000 structures with at least a 1 % annual chance of flooding, roughly three times more than are currently identified by FEMA as flood prone.

William Mobley et al.

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William Mobley et al.

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Short summary
In Southeast Texas, flood impacts are exacerbated by increases in impervious surfaces, human inaction, outdated FEMA-defined floodplains and modeling assumptions, as well as changing environmental conditions. The current flood maps are inadequate indicators of flood risk, especially in urban areas. This study proposed a novel method to model flood hazard and impact in urban areas. Specifically, we used novel flood risk modeling techniques to produce annualized flood hazard maps.
In Southeast Texas, flood impacts are exacerbated by increases in impervious surfaces, human...
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