Integration of transfer function model and back propagation neural network for forecasting storm sewer flow in Taipei metropolis

Chih Chiang Lu, Chu Hui Chen, Tian Chyi J. Yeh, Cheng Mau Wu, I. Fang Yau

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Typhoons and storms have often brought heavy rainfalls and induced floods that have frequently caused severe damage and loss of life in Taiwan. Our ability to predict sewer discharge and forecast floods in advance during storm seasons plays an important role in flood warning and flood hazard mitigation. In this paper, we develop an integrated model (TFMBPN) for forecasting sewer discharge that combines two traditional models: a transfer function model and a back propagation neural network. We evaluated the integrated model and the two traditional models by applying them to a sewer system of Taipei metropolis during three past typhoon events (NARI, SINLAKU, and NAKR). The performances of the models were evaluated by using predictions of a total of 6 h of sewer flow stages, and six different evaluation indices of the predictions. Finally, an overall performance index was determined to assess the overall performance of each model. Based on these evaluation indices, our analysis shows that TFMBNP yields accurate results that surpass the two traditional models. Thus, TFMBNP appears to be a promising tool for flood forecasting for the Taipei metropolis sewer system.

Original languageEnglish (US)
Pages (from-to)6-22
Number of pages17
JournalStochastic Environmental Research and Risk Assessment
Issue number1-2
StatePublished - Jan 2006


  • Back propagation neural network
  • Evaluation indexes
  • Overall performance index
  • Storms sewer flow
  • Transfer function model

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Water Science and Technology
  • Safety, Risk, Reliability and Quality
  • General Environmental Science


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