Filters: Contacts: Antonia Sebastian (X)
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This data release contains the associated data described in the related primary publication, “Predicting Flood Damage Probability Across the Conterminous United States” (Collins et al. [2022], see Related External Resources section). Publicly available geospatial datasets and random forest algorithms were used to analyze the spatial distribution and underlying drivers of flood damage probability caused by excessive rainfall and overflowing water bodies across the conterminous United States. Datasets contain input files for predictor and response variables used in the analysis and output files of flood damage probabilities generated from the analysis.
Abstract (from Environmental Research Letters): Floods are the leading cause of natural disaster damages in the United States, with billions of dollars incurred every year in the form of government payouts, property damages, and agricultural losses. The Federal Emergency Management Agency oversees the delineation of floodplains to mitigate damages, but disparities exist between locations designated as high risk and where flood damages occur due to land use and climate changes and incomplete floodplain mapping. We harnessed publicly available geospatial datasets and random forest algorithms to analyze the spatial distribution and underlying drivers of flood damage probability (FDP) caused by excessive rainfall and...
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