A novel application of a neuro-fuzzy computational technique in event-based rainfall-runoff modeling

  • Authors:

  • Amin Talei;Lloyd Hock Chye Chua;Chai Quek

  • Affiliations:

  • Nanyang Technological University, School of Civil and Environmental Engineering, Blk N1, #01C-70, 50, Nanyang Avenue, Singapore 639798, Singapore;Nanyang Technological University, School of Civil and Environmental Engineering, Blk N1, #01C-70, 50, Nanyang Avenue, Singapore 639798, Singapore;Centre for Computational Intelligence, Formerly the Intelligent Systems Laboratory, School of Computer Engineering, Nanyang Technological University, Blk N4, #2A-32, Nanyang Avenue, Singapore 6397 ...

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2010

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Abstract

Intelligent computing tools based on fuzzy logic and Artificial Neural Networks (ANN) have been successfully applied in various problems with superior performances. A new approach of combining these two powerful AI tools, known as neuro-fuzzy systems, has increasingly attracted scientists in different fields. Although many studies have been carried out using this approach in pattern recognition and signal processing, few studies have been undertaken to evaluate their performances in hydrologic modeling, specifically rainfall-runoff (R-R) modeling. This study presents an application of an Adaptive Network-based Fuzzy Inference System (ANFIS), as a neuro-fuzzy-computational technique, in event-based R-R modeling in order to evaluate the capabilities of this method for a sub-catchment of Kranji basin in Singapore. Approximately two years of rainfall and runoff data which from 66 separate rainfall events were analyzed in this study. Two different approaches in the selection criteria for calibration events were adopted and the performance of an ANFIS R-R model was compared against an established physically-based model called Storm Water Management Model (SWMM) in R-R modeling. The results of this study show that the selected neuro-fuzzy-computational technique (ANFIS) is comparable to SWMM in event-based R-R modeling. In addition, ANFIS is found to be better at peak flow estimation compared to SWMM. This study demonstrates the promising potential of neuro-fuzzy-computationally inspired hybrid tools in R-R modeling and analysis.