Journal article
Evaluation of probabilistic flow predictions in sewer systems using grey box models and a skill score criterion
Mathematical Statistics, Department of Informatics and Mathematical Modeling, Technical University of Denmark1
Department of Informatics and Mathematical Modeling, Technical University of Denmark2
Urban Water Engineering, Department of Environmental Engineering, Technical University of Denmark3
Department of Environmental Engineering, Technical University of Denmark4
Krüger Veolia Water Technologies5
In this paper we show how the grey box methodology can be applied to find models that can describe the flow prediction uncertainty in a sewer system where rain data are used as input, and flow measurements are used for calibration and updating model states. Grey box models are composed of a drift term and a diffusion term, respectively accounting for the deterministic and stochastic part of the models.
Furthermore, a distinction is made between the process noise and the observation noise. We compare five different model candidates’ predictive performances that solely differ with respect to the diffusion term description up to a 4 h prediction horizon by adopting the prediction performance measures; reliability, sharpness and skill score to pinpoint the preferred model.
The prediction performance of a model is reliable if the observed coverage of the prediction intervals corresponds to the nominal coverage of the prediction intervals, i.e. the bias between these coverages should ideally be zero. The sharpness is a measure of the distance between the lower and upper prediction limits, and skill score criterion makes it possible to pinpoint the preferred model by taking into account both reliability and sharpness.
In this paper, we illustrate the power of the introduced grey box methodology and the probabilistic performance measures in an urban drainage context.
Language: | English |
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Publisher: | Springer-Verlag |
Year: | 2012 |
Pages: | 1151-1162 |
ISSN: | 14363259 and 14363240 |
Types: | Journal article |
DOI: | 10.1007/s00477-012-0563-3 |
ORCIDs: | Møller, Jan Kloppenborg , Mikkelsen, Peter Steen and Madsen, Henrik |
Computational Intelligence Earth Sciences, general Environment Math. Appl. in Environmental Science Probability Theory and Stochastic Processes SC7 Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution