Learning Inter-Annual Flood Loss Risk Models From Historical Flood Insurance Claims and Extreme Rainfall Data. (arXiv:2212.08660v1 [cs.LG])

Flooding is one of the most disastrous natural hazards, responsible for
substantial economic losses. A predictive model for flood-induced financial
damages is useful for many applications such as climate change adaptation
planning and insurance underwriting. This research assesses the predictive
capability of regressors constructed on the National Flood Insurance Program
(NFIP) dataset using neural networks (Conditional Generative Adversarial
Networks), decision trees (Extreme Gradient Boosting), and kernel-based
regressors (Gaussian Process). The assessment highlights the most informative
predictors for regression. The distribution for claims amount inference is
modeled with a Burr distribution permitting the introduction of a bias
correction scheme and increasing the regressor’s predictive capability. Aiming
to study the interaction with physical variables, we incorporate Daymet
rainfall estimation to NFIP as an additional predictor. A study on the coastal
counties in the eight US South-West states resulted in an $R^2=0.807$. Further
analysis of 11 counties with a significant number of claims in the NFIP dataset
reveals that Extreme Gradient Boosting provides the best results, that bias
correction significantly improves the similarity with the reference
distribution, and that the rainfall predictor strengthens the regressor
performance.