English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Flooding is one of the most disastrous natural hazards, responsible forsubstantial economic losses. A predictive model for flood-induced financialdamages is useful for many applications such as climate change adaptationplanning and insurance underwriting. This research assesses the predictivecapability of regressors constructed on the National Flood Insurance Program(NFIP) dataset using neural networks (Conditional Generative AdversarialNetworks), decision trees (Extreme Gradient Boosting), and kernel-basedregressors (Gaussian Process). The assessment highlights the most informativepredictors for regression. The distribution for claims amount inference ismodeled with a Burr distribution permitting the introduction of a biascorrection scheme and increasing the regressor's predictive capability. Aimingto study the interaction with physical variables, we incorporate Daymetrainfall estimation to NFIP as an additional predictor. A study on the coastalcounties in the eight US South-West states resulted in an $R^2=0.807$. Furtheranalysis of 11 counties with a significant number of claims in the NFIP datasetreveals that Extreme Gradient Boosting provides the best results, that biascorrection significantly improves the similarity with the referencedistribution, and that the rainfall predictor strengthens the regressorperformance.

Learning Inter-Annual Flood Loss Risk Models From Historical Flood Insurance Claims and Extreme Rainfall Data