Using the Ensemble Kalman Filter to update a fast surrogate model for flow forecasting

Nadia Schou Vorndran Lund, Maurizio Mazzoleni, Henrik Madsen, Ole Mark, Peter Steen Mikkelsen, Dimitri Solomatine, Morten Borup

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Abstract

Many cities face issues with rain induced flooding and combined sewer overflows, which can be addressed by using hydrodynamic models. These models are often simplified in a real-time setting to make them faster, and their performance can be improved by using data assimilation. In this study we use the Ensemble Kalman Filter to update a simplified model of a small area of Copenhagen, Denmark. The model is
evaluated using perfect rain data for one summer month in 2016, and flow forecasts are used to quantify the performance of the update. We found that the 1-60 minutes forecast can be improved by updating the model. Having a small noise on the rain gives slightly worse results on a short forecast horizon and slightly better forecasts on a longer horizon. The forecast performance is also dependent on which model parts are updated.
Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Urban Drainage Modelling
EditorsG. Mannina
Place of PublicationPalermo, Italy
Publication date2018
Pages710-714
Publication statusPublished - 2018
Event11th International Conference on Urban Drainage Modelling - Palermo, Italy
Duration: 23 Sep 201826 Sep 2018

Conference

Conference11th International Conference on Urban Drainage Modelling
CountryItaly
CityPalermo
Period23/09/201826/09/2018

Keywords

  • Data assimilation
  • Surrogate model
  • Flow forecasting

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