FLOOD DAMAGE ASSESSMENT: A REVIEW OF MULTIVARIATE FLOOD DAMAGE MODELS

Abstract

Nowadays, more emphasis is given to flood risk management for dealing with flood disasters. To assess flood risk, damage from a flood is a crucial component to be considered. Flood damage function is a commonly accepted approach for the estimation of flood damages worldwide, where it combines the element of hazard, vulnerability, and exposure. However, this method usually considers only the flood depth and the type of buildings at risk. The effect of other flooding conditions (impact parameters) and the resistance parameters to the degree of flood damages are normally neglected. Flood risk assessment should cover all damage dimensions to obtain an extensive description of flood damages. Multivariate damage modeling can be applied to examine the relationship between flood damages and other flood influencing factors. This paper presents a review of methods applied to generate multivariate flood damage models, which includes the Multiple Linear Regression (MLR), Bayesian Network (BN), Artificial Neural Network (ANN), and the Random Forest (RF) analysis. Moreover, the multivariate models are also found capable of generating synthetic data to counter the problems of flood damage data scarcity experienced by developing countries. Identifying damage influencing factors, especially resistance parameters is important as a comprehensive flood damage model based on the local conditions and property characteristics of the study area can assist in the future damage assessment works, as well as offer decision-makers with an indispensable tool for managing strategies related to flood risk.