Flood Occurrence and Intensity Prediction in North America
Abstract
In this paper, we fit models to the occurrence and intensity of flood in North America to study risk for insurance purposes. North America is separated into 625 hydro basins with unique hydro graphic information. We collected basin features and climate data as predictors for flood occurrence and intensity. We chose both property losses and inundated areas as response variables for a flood. Our first goal is a predictive model for flood occurrence and intensity, and our second goal is to use this model with future regional climate model data to explore projected changes in flood risk due to climate change. The models we use are logistic regression and random forests. Logistic regression gave a better result and is chosen as the final model. We used a bias-corrected regional climate model to project future flood risk under a climate change scenario.
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hydro basin, property losses, quantile match, simulated climate data
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Wake Forest University