Application and validation of FLEMOcs – a flood-loss estimation model for the commercial sector

Seifert, Isabel; Kreibich, Heidi; Merz, Bruno; Thieken, Annegret H.

doi:10.1080/02626667.2010.536440

Cited by 57 publications

(77 citation statements)

References 15 publications

(19 reference statements)

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“…Based on the RMSE, a modified Weibull function was found to represent the data set best. This is in line with earlier studies in small catchments (Totschnig et al 2011;Papathoma-Köhle et al 2012; 2013) but in contrast with studies using damage classes (Jakob et al 2012) and studies in larger catchments using stepped vulnerability functions such as Kreibich et al (2010), Seifert et al (2010) or MURL (2000 based on a linear function and ICPR (2001) based on a quadratic equation. Finally, the modified Weibull function had shown a clear relation between the process magnitude and the degree of loss.…”

Section: Discussioncontrasting

confidence: 48%

“…These empirical models use observed data collected after an event by official authorities or insurance companies, or they are based on surveys, such as Fuchs et al (2007), Totschnig et al (2011) andPapathoma-Köhle et al (2012) for torrential flooding in the European Alps, Thieken et al (2008) and Kreibich et al (2010) for river flooding in Central Europe and Luino et al (2009) for flash floods in Southern Europe. Most of the studies performed were aiming in vulnerability assessment for either residential buildings (Totschnig et al 2011;Papathoma-Köhle et al 2012) or commercial buildings Seifert et al 2010), where some of the works were also targeted at hostels and hotels to mirror the importance of the tourism sector in individual case studies (Totschnig and Fuchs 2013).…”

Section: Introductionmentioning

confidence: 99%

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Micro-sized enterprises: vulnerability to flash floods

et al. 2016

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In the framework of risk assessment for flash floods, vulnerability is a key concept to assess the susceptibility of elements at risk. Vulnerability is defined as expected degree of loss for an element at risk due to a hazard impact of a defined magnitude and frequency. Besides the increasing number of studies on flash floods available, in-depth information on vulnerability was missing so far. In order to close this gap, a vulnerability model was created for micro-sized enterprises exposed to flash floods in Greece. This model was based on a nonlinear regression approach using data from four different events. By means of bootstrapping, different functions were fitted to the data, and a modified Weibull distribution was found to represent the relationship between process magnitude and degree of loss best. Moreover, there is no need to distinguish between different business sectors when computing vulnerability for buildings exposed. The model can be applied on a local scale and may serve as a basis for flash flood risk management.

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Section: Discussioncontrasting

confidence: 48%

Section: Introductionmentioning

confidence: 99%

Micro-sized enterprises: vulnerability to flash floods

et al. 2016

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“…From this perspective, flood damage models validation is hardly performed, the main constraint being the limited availability 10 of high quality (damage) data (Merz et al, 2010;Jongman et al, 2012;Meyer et al, 2013;Molinari et al, 2014); availability that further decreases whit the increase of the resolution at which damage models work (de Moel et al, 2015) and when indirect and intangible damage is considered.…”

Section: Damage Modelsmentioning

confidence: 99%

“…Next to probabilities, the hazard parameters inundation depth, flow velocity and flood extension are usually required for flood risk management decisions and therefore validation efforts concentrate on them (see, for instance, Thieken et al, 2005;Elmer et al, 2010;Merz et al, 2010;Merz et al 2013;and section 3.3).…”

mentioning

confidence: 99%

Review Article: Validation of flood risk models: current practice and innovations

Molinari

Bruijn

Castillo

et al. 2017

Preprint

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Abstract.Although often neglected, model validation is a key topic in flood risk analysis, as flood risk estimates are characterised by significant levels of uncertainty. In this paper, we discuss the state of art of flood risk models validation, as concluded from the discussion among more than 50 experts at two main scientific events. The events aimed at identifying policy and research recommendations towards promoting more common practice of validation, and an improvement of flood 15 risk models reliability. We pay specific attention to the different components of the risk modelling chain (i.e. flood hazard, defence failure and flood damage analysis) as well as to their role into risk estimates, to highlight specificities and commonalities with respect to implemented techniques and research needs. The main conclusions from this review can be summarised as the need of higher quality data to perform validation and of benchmark solutions to be followed in different contexts, along with a greater involvement of end-users in the debate on flood risk models validation. 20

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“…The RMSE also expresses the variation of the estimated ratios from the observed ratios. It signifies the standard deviation of the differences between the modelled values and observed values [41,66]. …”

Section: Performance Of the Applied Damage Modelsmentioning

confidence: 99%

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

Nafari

Ngo

Mendis

2016

Water

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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.

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Application and validation of FLEMOcs – a flood-loss estimation model for the commercial sector

Cited by 57 publications

References 15 publications

Micro-sized enterprises: vulnerability to flash floods

Micro-sized enterprises: vulnerability to flash floods

Review Article: Validation of flood risk models: current practice and innovations

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

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