Runoff Modelling in Urban Storm Drainage by Neural Networks

Michael R. Rasmussen, Michael Brorsen, Kjeld Schaarup-Jensen

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

A neural network is used to simulate folw and water levels in a sewer system. The calibration of th neural network is based on a few measured events and the network is validated against measureed events as well as flow simulated with the MOUSE model (Lindberg and Joergensen, 1986). The neural network is used to compute flow or water level at selected points in the sewer system, and to forecast the flow from a small residential area. The main advantages of the neural network are the build-in self calibration procedure and high speed performance, but the neural network cannot be used to extract knowledge of the runoff process. The neural network was found to simulate 150 times faster than e.g. the MOUSE model.
Original languageEnglish
Title of host publicationNOVATECH 95 : 2nd International Conference on Innovative Technologies in Urban Storm Drainage
Number of pages8
PublisherGRAIE
Publication date1995
Pages395-402
ISBN (Print)295093370X
Publication statusPublished - 1995
EventInternational Conference on Innovative Technologies in Urban Storm Drainage - Lyon, France
Duration: 30 May 19951 Jun 1995
Conference number: 2

Conference

ConferenceInternational Conference on Innovative Technologies in Urban Storm Drainage
Number2
Country/TerritoryFrance
CityLyon
Period30/05/199501/06/1995

Keywords

  • MOUSE model
  • Runoff model
  • Neural networks

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