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Verification of flood damage modelling using insurance data
Q Zhou1, T E Panduro, B J Thorsen
1Department of Environmental Engineering, Technical University of Denmark, Denmark. qiaz@env.dtu.dk
Summary
Insurance data analysis reveals rainfall statistics alone cannot predict individual claim costs but can model daily costs. Combining insurance and regional data improves hazard mapping and claim prediction, highlighting the need for better data and collaboration.
Area of Science:
- Environmental science
- Risk management
- Insurance analytics
Background:
- Insurance data is crucial for understanding damage and verifying risk models.
- Previous studies have explored rainfall-induced damage but often lack integrated datasets.
Purpose of the Study:
- To analyze insurance data for damage description and risk model verification.
- To assess the feasibility of using local rainfall statistics for cost modeling.
- To explore the relationship between insurance claims, regional data, and hazard maps.
Main Methods:
- Analysis of insurance claim data from a Danish case study.
- Statistical modeling of rainfall data against individual claim costs and daily costs.
- Integration of insurance data with regional datasets and hazard maps.
Main Results:
- Local rainfall statistics are insufficient for predicting individual claim costs.
- Rainfall statistics are feasible for modeling overall daily costs per claim.
- Combining insurance and regional data reveals clear links between claims and hazard maps.
- Improved data collection and analysis, including socioeconomic variables, can enhance damage cost prediction.
Conclusions:
- Enhanced data collection and analytical methods are key to improving damage cost prediction.
- Collaboration between scientific research and insurance agencies is essential for advancing inundation modeling and urban drainage economic assessments.
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