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

Abstract

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 (FLFA rs ) 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.

Flood loss modelling with FLF-IT: a new flood loss function for Italian residential structures

“…Direct damage takes place when the floodwater physically inundates buildings and structures, whereas indirect damage accounts for the consequences of direct damage on a wider scale of space and time (Hasanzadeh Nafari et al, 2016c). The tools employed to assess flood risk consist of a variety of damage models, with differing methods depending on the type of accounted losses.…”

Predictive applications of Australian flood loss models after a temporal and spatial transfer

“…Regression trees were drawn based on the approach of Hasanzadeh Nafari et al (2016c). Compared to the outcomes of that study, the model has been redeveloped, and its shape has been adapted based Table 1.…”

“…Merz et al (2013) have classified these parameters into flood intensity factors including depth of water, flow velocity, return period, duration, and contamination of water; and building flood-resistant indicators including material and characteristics of property, individual precaution and emergency actions, early warning time and preparedness, former flood experience of residents, and residents' socio-economic situations (Merz et al 2013). Accordingly, data mining techniques, as effective alternatives to traditional stage-damage functions, have recently been used for exploring the interaction and the importance of different damage-influencing parameters in Germany, the Mekong Delta, and Australia (Merz et al 2013;Chinh et al 2015;Hasanzadeh Nafari et al 2016c;Kreibich et al 2016). These studies show that the impacts of different affecting factors can be studied effectively with the treebased data mining technique, which is mostly utilized in water resource studies and hydrology science, but rarely in flood-loss modelling (Merz et al 2013).…”

Analysing flood fatalities in Vietnam using national disaster database and tree-based methods

“…Recently they have been applied in hydrology studies (Ali et al, 2010;Carlisle et al, 2010;Loos and Elsenbeer, 2011) and flood risk studies (Merz et al, 2013;Spekkers et al, 2014;Chinh et al, 2015;Hasanzadeh Nafari et al, 2016;Wagenaar et al, 2017). A review of recent literature on the topic reveals that applications in flood risk studies are very recent.…”

“…A review of recent literature on the topic reveals that applications in flood risk studies are very recent. Four in five articles used tree-based methods to select the substantial flood damage influencing parameters for different case studies 20 using MATLAB software (Merz et al, 2013;Chinh et al, 2015;Hasanzadeh Nafari et al, 2016;Wagenaar et al, 2017). Spekkers et al (2014) explored damage-influencing factors on insurance claims regarding water-related damage using decision-tree analysis and variable importance with statistical software R. There has not been any empirical study on the application of tree-based methods approach to analyse the damage-influencing parameters on flood fatalities.…”