Residential building and sub-building level flood damage analysis using simple and complex models

Flood damage assessment is critical for optimal risk management investments. Damage models evaluate physical damage or monetary loss from direct building exposure to flood hazard processes. Traditional models represent a simple relationship whereby physical damage increases with water depth. More complex models offer an improved understanding of vulnerability, analysing interactions between multiple hazard and exposure variables that drive damage. Our study investigates whether increasing model complexity and explanatory damage variables improves prediction precision and reliability at residential building and sub-building (component) levels. We evaluate simple and complex empirical univariable and multivariable models for flood damage prediction. The Random Forest algorithm learned on multiple hazard and exposure explanatory variables outperformed linear and non-linear univariable regression approaches. Random Forest model predictive precision was highest when learning was limited to water depth and several important explanatory damage variables (flow velocity, area and floor height). Component damage models demonstrated high predictive precision for internal finishes and services. Precision reduced for structure and external finishes as damage samples for model learning were limited. High performing but complex multivariable models require further spatio-temporal transfer investigation to determine opportunities for accurate and reliable object-specific flood damage prediction in data scarce locations.