Evaluation of probabilistic flow predictions in sewer systems using grey box models and a skill score criterion

Thordarson, Fannar Ørn; Breinholt, Anders; Møller, Jan Kloppenborg; Mikkelsen, Peter Steen; Grum, Morten; Madsen, Henrik

doi:10.1007/s00477-012-0563-3

Cited by 21 publications

(12 citation statements)

References 27 publications

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“…How to derive confidence bounds in simulation was described in detail in Thordarson et al (2012) and will not be repeated here. Again the inverse transformation will have to be taken as described above in Section 4.1.2 once the confidence bounds of the Z-domain has been found.…”

Section: Model M3mentioning

confidence: 99%

“…The evaluation of the sharpness and the reliability can be combined into a single scoring criterion denoted the interval skill score. In the following the performance measures are formulated for simulation uncertainty evaluation, however it should be kept in mind that the notation is slightly different when formulated for the one step prediction evaluation (see Thordarson et al (2012)). …”

Section: Probabilistic Performance Measuresmentioning

confidence: 99%

“…To measure each models ability to generate useful confidence bounds we adopt the performance measures described in Thordarson et al (2012). We are generally interested in the width of the derived confidence bounds referred to as the sharpness and the correspondence between nominal and observed coverage of the confidence bounds referred to as the reliability.…”

Section: Probabilistic Performance Measuresmentioning

confidence: 99%

“…These measures take the coverage, the width of the bounds and the symmetry of the bounds into account; however none of them can individually be used to determine which model of several candidate models that provides the best predictions/simulations and clearly there seems to be a need for such a measure. We evaluate the derived uncertainty bounds using the reliability, sharpness and interval skill scoring criterion of Gneiting and Raftery (2007) and Thordarson et al (2012) and we compare the performance of a model tailored for prediction and a model tailored for simulation to clearly distinguish the difference between the two.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A formal statistical approach to representing uncertainty in rainfall–runoff modelling with focus on residual analysis and probabilistic output evaluation – Distinguishing simulation and prediction

Breinholt

Møller

Madsen

et al. 2012

Journal of Hydrology

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Section: Model M3mentioning

confidence: 99%

Section: Probabilistic Performance Measuresmentioning

confidence: 99%

Section: Probabilistic Performance Measuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A formal statistical approach to representing uncertainty in rainfall–runoff modelling with focus on residual analysis and probabilistic output evaluation – Distinguishing simulation and prediction

Breinholt

Møller

Madsen

et al. 2012

Journal of Hydrology

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“…Accordingly, the model performance shows a perfect reliability if the HR is equal to 0.9, i.e. the nominal coverage rate of 90% of model outcomes is equal to the coverage rate of actual damage records (Thordarson et al 2012). The models' evaluation criteria are calculated based on Table 2.…”

Section: Validation Of the Modelsmentioning

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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How useful are complex flood damage models?

Schröter

Kreibich

Vogel

et al. 2014

Water Resources Research

158

215

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We investigate the usefulness of complex flood damage models for predicting relative damage to residential buildings in a spatial and temporal transfer context. We apply eight different flood damage models to predict relative building damage for five historic flood events in two different regions of Germany. Model complexity is measured in terms of the number of explanatory variables which varies from 1 variable up to 10 variables which are singled out from 28 candidate variables. Model validation is based on empirical damage data, whereas observation uncertainty is taken into consideration. The comparison of model predictive performance shows that additional explanatory variables besides the water depth improve the predictive capability in a spatial and temporal transfer context, i.e., when the models are transferred to different regions and different flood events. Concerning the trade-off between predictive capability and reliability the model structure seem more important than the number of explanatory variables. Among the models considered, the reliability of Bayesian network-based predictions in space-time transfer is larger than for the remaining models, and the uncertainties associated with damage predictions are reflected more completely.

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Evaluation of probabilistic flow predictions in sewer systems using grey box models and a skill score criterion

Cited by 21 publications

References 27 publications

A formal statistical approach to representing uncertainty in rainfall–runoff modelling with focus on residual analysis and probabilistic output evaluation – Distinguishing simulation and prediction

A formal statistical approach to representing uncertainty in rainfall–runoff modelling with focus on residual analysis and probabilistic output evaluation – Distinguishing simulation and prediction

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

How useful are complex flood damage models?

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