An Assessment of the Effectiveness of Tree-Based Models for Multi-Variate Flood Damage Assessment in Australia

Roozbeh Hasanzadeh Nafari,Tuan Ngo,Priyan Mendis

doi:10.3390/w8070282

Abstract

Flood is a frequent natural hazard that has significant financial consequences for Australia. In Australia, physical losses caused by floods are commonly estimated by stage-damage functions. These methods usually consider only the depth of the water and the type of buildings at risk. However, flood damage is a complicated process, and it is dependent on a variety of factors which are rarely taken into account. This study explores the interaction, importance, and influence of water depth, flow velocity, water contamination, precautionary measures, emergency measures, flood experience, floor area, building value, building quality, and socioeconomic status. The study uses tree-based models (regression trees and bagging decision trees) and a dataset collected from 2012 to 2013 flood events in Queensland, which includes information on structural damages, impact parameters, and resistance variables. The tree-based approaches show water depth, floor area, precautionary measures, building value, and building quality to be important damage-influencing parameters. Furthermore, the performance of the tree-based models is validated and contrasted with the outcomes of a multi-parameter loss function (FLFArs) from Australia. The tree-based models are shown to be more accurate than the stage-damage function. Consequently, considering more parameters and taking advantage of tree-based models is recommended. The outcome is important for improving established Australian flood loss models and assisting decision-makers and insurance companies dealing with flood risk assessment.

Highlights

In recent decades, flood risk is growing, due to climate change and increase in vulnerability of properties at risk [1,2,3]
The results of this study revealed that the depth of water, area of buildings, return period of flood, contamination, duration of flooding, and precautionary measures, respectively, have the highest influences on flood loss assessment in the region of study [15,26]
Regression trees and bagging decision trees were applied to determine the prominent damage‐influencing parameters, to understand their effect on the extent of structural damage, and to compare the performance of the tree‐based models with an established flood loss function

Summary

Introduction

Flood risk is growing, due to climate change and increase in vulnerability of properties at risk [1,2,3]. Flood risk management is attracting more attention [7,8,9], and results are used to inform disaster management policy and support the development of risk reduction measures [10,11]. Flood risk management has to be based upon an appropriate evaluation of flood hazard and flood vulnerability [12,13], including an assessment of damage and effectiveness of risk reduction measures [14,15,16]. Loss estimation and consequence assessment is an indispensable part of flood risk management [17,18].

Objectives

Methods

Results

Conclusion