@mastersthesis {bnh-5019, title = {Flood damage assessment in urban areas}, year = {2018}, month = {01/2018}, pages = {247}, school = {University of Melbourne}, address = {Melbourne}, abstract = {

Natural disaster prevention activities are attracting greater priority since prevention is more cost-effective and less uncertain than response, and aligned with the vision and mission of sustainable development. Increasing the resilience of communities and businesses is dependent on the extension of structural and non-structural risk mitigation activities. Hence, the nation-wide frameworks of natural disaster risk management are promoting a global movement from reactive activities (response and recovery) to proactive actions (prevention and mitigation). In Australia, flood risk management is of high priority since flood is a frequent natural hazard with significant financial consequences. Flood risk assessment and flood damage estimation are the primary steps in the flood risk management process because they are essential for the identification and prioritisation of top priority areas, cost-benefit analysis, checking the feasibility of risk mitigation options, selecting best practices in risk reduction and land use planning. This research aims to develop a validated flood damage assessment framework for the geographical area of Australia using historical data collected in several disaster events to inform disaster management policy in support of the development of risk reduction measures. In Australia, due to a lack of empirical data, most damage models are not calibrated with real damage data, and few studies have been conducted on the validation of results. In addition, most approaches are absolute, which is quite rigid and does not easily transfer across time and space. All approaches are of the traditional type, which relies on a deterministic relationship between type or use of the properties at risk and the depth of water. Thus, the interaction of most damage-influencing parameters and the uncertainty of data are neglected. This study has attempted to address these issues and the knowledge gaps.

Firstly, a comprehensive empirical data set including information on damage extent, flood impact variables and resistance factors was collected, and data mining, data preparation and data transformation were conducted. Since the function approach is a common and internationally accepted methodology for estimating the value of flood losses, some new relative multi-parameter flood damage assessment functions were derived, calibrated and validated for the most common residential and commercial building types in Australia. The functions were developed using the bootstrapping approach and considered the inherent uncertainty in the data sample. The performance of the new flood loss functions, in comparison to the empirical data, was contrasted with that of well-known flood damage assessment models from overseas and Australia. The new model was then transferred to a study area in Italy to check the ease of using local empirical data, evaluating the accuracy of the outcome, and assessing the ability to change parameters based on building practices across the world. Flood damage assessment is a complicated process and can be dependent on a variety of parameters which are not considered in stage-damage functions. Accordingly, a treebased model was developed for exploring the interaction, importance and influence of other damage-influencing parameters on the extent of losses. Finally, the candidate has explored the predictive performance of the new approaches (i.e. flood loss functions and tree-based flood loss models) in assessing the extent of physical damages after temporal and spatial transfer. The predictive power of these models was tested for precision, variation and reliability, and was also checked for some sub-classes of water depth and some groups of building type. The advantages of the newly derived stage-damage functions compared to the existing Australian models include: calibration with empirical data, greater accuracy in results, a better level of transferability in time and space, consideration of the epistemic uncertainty of data, transparency of the logic behind the model and the ability to change parameters based on building practices across the world. Furthermore, results of the tree-based analysis showed that while water depth is the most significant damage predictor in the area of study, floor space, private precautionary measures, building value and building quality also correlate with the extent of flood losses.

Also, the tree based models are shown to be more accurate than the stage-damage function. Thus, considering more parameters and taking advantage of tree-based models are recommended. Finally, it has been shown that considering more details of the damaging process can be useful for enhancing the level of transferability of damage models in time and/or space. Overall, this thesis presents a significant contribution to the flood damage assessment process by offering a calibrated and validated flood loss estimation framework. The results provide the input data for subsequent damage reduction, vulnerability mitigation and disaster risk reduction.

}, author = {Roozbeh Hasanzadeh Nafari} } @article {bnh-5020, title = {Predictive applications of Australian flood loss models after a temporal and spatial transfer}, journal = {Geomatics, Natural Hazards and Risk }, volume = {9}, year = {2018}, month = {03/2018}, pages = {14}, chapter = {416}, abstract = {

In recent decades, considerably greater flood losses have increased attention to flood risk evaluation. This study used data-sets collected from Queensland flood events and investigated the predictive capacity of three new Australian flood loss models to assess the extent of physical damages, after a temporal and spatial transfer. The models{\textquoteright} predictive power is tested for precision, variation, and reliability. The performance of a new Australian flood loss function was contrasted with two tree-based damage models, one pruned and one un-pruned. The tree-based models are grown based on the interaction of flood loss ratio with 13 examined predictors gathered from flood specifications, building characteristics, and mitigation actions. Besides an overall comparison, the prediction capacity is also checked for some sub-classes of water depth and some groups of building-type.

It has been shown that considering more details of the flood damage process can improve the predictive capacity of damage prediction models. In this regard, complexity with parameters with low predictive power may lead to more uncertain results. On the other hand, it has also been demonstrated that the probability analysis approach can make damage models more reliable when they are subjected to use in different flooding events.

}, keywords = {damage assessment, Disaster risk reduction, flood risk, Natural hazards, predictive capacity}, url = {https://www.tandfonline.com/doi/full/10.1080/19475705.2018.1445666}, author = {Roozbeh Hasanzadeh Nafari and Tuan Ngo} } @article {bnh-3795, title = {Flood loss modelling with FLF-IT: a new flood loss function for Italian residential structures}, journal = {Natural Hazards and Earth Systems Sciences}, volume = {17}, year = {2017}, month = {07/2017}, pages = {1047-1059}, chapter = {1047}, abstract = {

The damage triggered by different flood events costs the Italian economy millions of euros each year. This cost is likely to increase in the future due to climate variability and economic development. In order to avoid or reduce such significant financial losses, risk management requires tools which can provide a reliable estimate of potential flood impacts across the country. Flood loss functions are an internationally accepted method for estimating physical flood damage in urban areas. In this study, we derived a new flood loss function for Italian residential structures (FLF-IT), on the basis of empirical damage data collected from a recent flood event in the region of Emilia-Romagna. The function was developed based on a new Australian approach (FLFA), which represents the confidence limits that exist around the parameterized functional depth{\textendash}damage relationship. After model calibration, the performance of the model was validated for the prediction of loss ratios and absolute damage values. It was also contrasted with an uncalibrated relative model with frequent usage in Europe. In this regard, a three-fold cross-validation procedure was carried out over the empirical sample to measure the range of uncertainty from the actual damage data. The predictive capability has also been studied for some sub-classes of water depth. The validation procedure shows that the newly derived function performs well (no bias and only 10 \% mean absolute error), especially when the water depth is high. Results of these validation tests illustrate the importance of model calibration. The advantages of the FLF-IT model over other Italian models include calibration with empirical data, consideration of the epistemic uncertainty of data, and the ability to change parameters based on building practices across Italy.

}, doi = {https://doi.org/10.5194/nhess-17-1047-2017}, url = {http://www.nat-hazards-earth-syst-sci.net/17/1047/2017/}, author = {Roozbeh Hasanzadeh Nafari and Mattia Amadio and Tuan Ngo and Jaroslav Mysiak} } @article {bnh-2916, title = {An assessment of the effectiveness of tree-based models for multi-variate flood damage assessment in Australia}, journal = {Water}, volume = {8}, year = {2016}, month = {07/2016}, chapter = {282}, 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.

}, doi = {10.3390/w8070282 }, url = {http://www.mdpi.com/2073-4441/8/7/282}, author = {Roozbeh Hasanzadeh Nafari and Tuan Ngo and Priyan Mendis} } @article {bnh-2657, title = {Calibration and validation of FLFArs {\textendash} a new flood loss function for Australian residential structures}, journal = {Natural Hazards and Earth System Sciences}, volume = {16}, year = {2016}, month = {01/2016}, pages = {15-27}, chapter = {15}, abstract = {

Rapid urbanisation, climate change and unsustain- able developments are increasing the risk of floods. Flood is a frequent natural hazard that has significant financial con- sequences for Australia. The emergency response system in Australia is very successful and has saved many lives over the years. However, the preparedness for natural disaster im- pacts in terms of loss reduction and damage mitigation has been less successful.

In this paper, a newly derived flood loss function for Australian residential structures (FLFArs ) has been presented and calibrated by using historic data collected from an extreme event in Queensland, Australia, that occurred in 2013. After- wards, the performance of the method developed in this work (contrasted to one Australian model and one model from USA) has been compared with the observed damage data collected from a 2012 flood event in Maranoa, Queensland. Based on this analysis, validation of the selected methodolo- gies has been performed in terms of Australian geographical conditions.

Results obtained from the new empirically based function (FLFArs ) and the other models indicate that it is apparent that the precision of flood damage models is strongly dependent on selected stage damage curves, and flood damage estima- tion without model calibration might result in inaccurate predictions of losses. Therefore, it is very important to be aware of the associated uncertainties in flood risk assessment, es- pecially if models have not been calibrated with real damage data.

}, doi = {10.5194/nhess-16-15-2016}, url = {http://www.nat-hazards-earth-syst-sci.net/16/15/2016/}, author = {Roozbeh Hasanzadeh Nafari and Tuan Ngo and Lehman, W.} } @article {bnh-2725, title = {Development and evaluation of FLFAcs - A new Flood Loss Function for Australian commercial structures}, journal = {International Journal of Disaster Risk Reduction,}, volume = {17}, year = {2016}, month = {08/2016}, pages = {13-23}, chapter = {13}, abstract = {

Commercial building flood losses significantly affect the Australian economy; however, there are not many models for commercial flood damage estimation and their results are not reliable. This study has attempted to derive and develop a new model (FLFAcs) for estimating the magnitude of direct damage on commercial structures. The FLFAcs\ {\textendash} Flood Loss Function for Australian commercial structures, was calibrated using empirical data collected from the 2013 flood in Bundaberg, Australia, and considering the inherent uncertainty in the data sample. In addition, the newly derived model has been validated using a K-fold cross-validation procedure. The model performance has also been compared with the Flood Loss Estimation MOdel for the commercial sector (FLEMOcs) and the Federal Emergency Management Agency (FEMA) damage functions from overseas, as well as the ANUFLOOD damage model from Australia.

The validation procedure shows very good results for FLFAcs\ performance (no bias and only five per cent mean absolute error). It also shows that ANUFLOOD, as Australia{\textquoteright}s most prevalently used commercial loss estimation model, is still subject to very high uncertainty. Hence, there is an immediate need for a project to build new depth{\textendash}damage functions for commercial and industrial properties.

Awareness of these issues is important for strategic decision-making in flood risk reduction and it could amplify the cognition of decision-makers and insurance companies about flood risk assessment in Australia.

}, doi = {10.1016/j.ijdrr.2016.03.007}, url = {http://www.sciencedirect.com/science/article/pii/S221242091530131X}, author = {Roozbeh Hasanzadeh Nafari}, editor = {Tuan Ngo and Lehman, W.} }