Forecast generation for real-time control of urban drainage systems using greybox modelling and radar rainfall

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

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Forecast generation for real-time control of urban drainage systems using greybox modelling and radar rainfall. / Löwe, Roland; Mikkelsen, Peter Steen; Madsen, Henrik.

Proceedings of the 10th International Conference on Hydroinformatics. 2012.

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

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Löwe, Roland; Mikkelsen, Peter Steen; Madsen, Henrik / Forecast generation for real-time control of urban drainage systems using greybox modelling and radar rainfall.

Proceedings of the 10th International Conference on Hydroinformatics. 2012.

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

Bibtex

@inbook{fe382e3eca9b4db4b0fb9cc57e7c3a2a,
title = "Forecast generation for real-time control of urban drainage systems using greybox modelling and radar rainfall",
author = "Roland Löwe and Mikkelsen, {Peter Steen} and Henrik Madsen",
year = "2012",
booktitle = "Proceedings of the 10th International Conference on Hydroinformatics",

}

RIS

TY - GEN

T1 - Forecast generation for real-time control of urban drainage systems using greybox modelling and radar rainfall

A1 - Löwe,Roland

A1 - Mikkelsen,Peter Steen

A1 - Madsen,Henrik

AU - Löwe,Roland

AU - Mikkelsen,Peter Steen

AU - Madsen,Henrik

PY - 2012

Y1 - 2012

N2 - We present stochastic flow forecasts to be used in a real-time control setup for urban drainage systems. The forecasts are generated using greybox models with rain gauge and radar rainfall observations as input. Predictions are evaluated as intervals rather than just mean values. We obtain satisfactory predictions for the smaller catchment but rather large uncertainties for the bigger catchment where the applied storage cascade seems too simple. Radar rainfall introduces more uncertainty into the flow forecast model estimation. However, the radar rainfall forecasts also result in a slightly improved point prediction of<br/>flows which we aim to exploit with a modified estimation approach in the future.

AB - We present stochastic flow forecasts to be used in a real-time control setup for urban drainage systems. The forecasts are generated using greybox models with rain gauge and radar rainfall observations as input. Predictions are evaluated as intervals rather than just mean values. We obtain satisfactory predictions for the smaller catchment but rather large uncertainties for the bigger catchment where the applied storage cascade seems too simple. Radar rainfall introduces more uncertainty into the flow forecast model estimation. However, the radar rainfall forecasts also result in a slightly improved point prediction of<br/>flows which we aim to exploit with a modified estimation approach in the future.

BT - Proceedings of the 10th International Conference on Hydroinformatics

T2 - Proceedings of the 10th International Conference on Hydroinformatics

ER -