Predicting Flood Property Insurance Claims over CONUS, Fusing Big Earth Observation Data Journal Article uri icon

Overview

abstract

  • Abstract; Each year throughout the contiguous United States (CONUS), flood hazards cause damage amounting to billions of dollars in homeowner insurance claims. As climate change threatens to raise the frequency and severity of flooding in vulnerable areas, the ability to predict the number of property insurance claims resulting from flood events becomes increasingly important to flood resilience. Based on random forest, we develop a flood property Insurance Claims model (iClaim) by fusing records from the National Flood Insurance Program (NFIP), including building locations, topography, basin morphometry, and land cover, with data from multiple sources of hydrometeorological variables, including flood extent, precipitation, and operational river-stage and oceanic water-level measurements. The model utilizes two steps—damage level classification and claim number regression—and subsampling strategies designed accordingly to reduce overfitting and underfitting caused by the flood claim samples, which are unevenly distributed and widely ranged. We evaluate the model using 446,446 grid samples identified from 589 flood events occurring from 2016 to 2019 over CONUS, overlapping 258,159 claims out of a total of 287,439 NFIP records of the same period. Our rigorous validation yields acceptable performance at the grid/event, county/event, and event accumulative level, with R2 over 0.5, 0.9, and 0.95, respectively. We conclude that the iClaim model can be used in many application scenarios, including assessing flood impact and improving flood resilience.

publication date

  • March 1, 2022

Date in CU Experts

  • December 21, 2021 9:12 AM

Full Author List

  • Yang Q; Shen X; Yang F; Anagnostou EN; He K; Mo C; Seyyedi H; Kettner AJ; Zhang Q

author count

  • 9

Other Profiles

International Standard Serial Number (ISSN)

  • 0003-0007

Electronic International Standard Serial Number (EISSN)

  • 1520-0477

Additional Document Info

start page

  • E791

end page

  • E809

volume

  • 103

issue

  • 3