Calibration and validation of FLFA&amp;lt;sub&amp;gt;rs&amp;lt;/sub&amp;gt; -- a new flood loss function  for Australian residential structures

Nafari, Roozbeh Hasanzadeh; Ngo, Tuan; Lehman, W. F.

doi:10.5194/nhess-16-15-2016

Cited by 32 publications

(27 citation statements)

References 35 publications

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“…Figure 6. Comparison of the flood damage estimation models' precision per water-depth class (RMSE: root mean square error; number of samples for each sub-class of water depth, respectively, is 14, 36, 52, 96, 125, 222, and 68). of study (Hasanzadeh Nafari et al, 2016b;McBean et al, 1986).…”

Section: Results Comparison and Model Validationmentioning

confidence: 97%

“…r and D max , respectively), with reference to the empirical dataset (Hasanzadeh Nafari et al, 2016a). The selection will be made by the chi-square test of goodness of fit to minimize predictive errors.…”

Section: Calibration Of Flf-itmentioning

confidence: 99%

“…Also, instead of a deterministic regression analysis, this study has relied on the probabilistic relationship among the percentage of damage and other damage-related parameters (i.e. building and flood characteristics) (Hasanzadeh Nafari et al, 2016b). In this regard, a bootstrapping approach has been employed to resample the damage data 1000 times.…”

Section: Calibration Of Flf-itmentioning

confidence: 99%

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

Nafari

Amadio

Ngo

et al. 2017

Nat. Hazards Earth Syst. Sci.

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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-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 threefold 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.

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Section: Results Comparison and Model Validationmentioning

confidence: 97%

Section: Calibration Of Flf-itmentioning

confidence: 99%

Section: Calibration Of Flf-itmentioning

confidence: 99%

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

Nafari

Amadio

Ngo

et al. 2017

Nat. Hazards Earth Syst. Sci.

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“…The Flood Loss Function for Australian residential structures (FLFA rs ) was newly developed by Hasanzadeh Nafari et al (2016b). The FLFA rs is an empirical-synthetic model, meaning that this model was initially developed using a simplified synthetic approach called the sub-assembly method, developed by the HAZUS manual (FEMA 2012).…”

Section: Flfa Rsmentioning

confidence: 99%

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

Nafari

Ngo

2018

Geomatics, Natural Hazards and Risk

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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' 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.

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“…In order to reduce the uncertainty problems. In the last years, some applications of this method to flood risk have been performed (see Merz et al, 2013;Chinh et al, 2016;Hasanzadeh Nafari et al, 2016Kreibich et al, 2017;Spekkers et al, 2014), but literature in this field is still scarce if compared to the numerous studies that use simpler uni-variable models. Nevertheless, Merz et al (2013) demonstrated that tree based models are able to improve the performance of existing models like stage-damage functions and to better identify the most informative independent variables and their interactions (e.g., they can identify different importance 25 levels of a same variable, depending on the value of another variable).…”

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confidence: 99%

Development and assessment of uni- and multi-variable flood loss models for Emilia-Romagna (Italy)

Carisi¹,

Schröter²,

Domeneghetti³

et al. 2017

Preprint

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Abstract.Simplified flood loss models are one important source of uncertainty in flood risk assessments. Many countries experience sparseness or absence of comprehensive high-quality flood loss data sets which is often rooted in a lack of protocols and reference procedures for compiling loss data sets after flood events. Such data are an important reference for developing and validating flood loss models. We consider the Secchia river flood event of January 2014, when a sudden levee-breach caused the 5 inundation of nearly 52 km 2 in Northern Italy. For this event we compiled a comprehensive flood loss data set of affected private households including buildings footprint, economic value, damages to contents, etc. based on information collected by local authorities after the event. By analysing this data set we tackle the problem of flood damage estimation in Emilia-Romagna (Italy) by identifying empirical uni-and multi-variable loss models for residential buildings and contents. The accuracy of the proposed models is compared with those of several flood-damage models reported in the literature, providing additional 10 insights on the transferability of the models between different contexts. Our results show that (1) even simple uni-variable damage models based on local data are significantly more accurate than literature models derived for different contexts; (2) multi-variable models that consider several explanatory variables outperform uni-variable models which use only water depth.However, multi-variable models can only be effectively developed and applied if sufficient and detailed information is available.

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Calibration and validation of FLFA<sub>rs</sub> -- a new flood loss function for Australian residential structures

Cited by 32 publications

References 35 publications

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

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

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

Development and assessment of uni- and multi-variable flood loss models for Emilia-Romagna (Italy)

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