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Using An Integrated Fuzzy Inference System And Artificial Neural Network To Forecast Daily Discharge

Author

Listed:
  • Chang-Shian Chen

    (Department of Water Resources Engineering, Feng Chia University)

  • You-Da Jhong

    (Graduate Institute of Civil and Hydraulic Engineering, Feng Chia University)

Abstract

Given the nonlinearity and uncertainty in the rainfall-runoff process, estimating or predicting hydrologic data often encounters tremendous difficulty. This study applied fuzzy theory to create a daily flow forecasting model. To improve the time-consuming definition process of membership function, which is usually concluded by a trial-and-error approach, this study designated the membership function by artificial neural network (ANN) with either a supervised or unsupervised learning procedure. The supervised learning was processed by the adaptive network based fuzzy inference system (ANFIS), while the unsupervised learning was created by fuzzy and self-organizing map (SOMFIS). The results indicate that the ANFIS method under increment flow data could provide more precise results for daily flow forecasting.

Suggested Citation

  • Chang-Shian Chen & You-Da Jhong, 2007. "Using An Integrated Fuzzy Inference System And Artificial Neural Network To Forecast Daily Discharge," Portuguese Journal of Management Studies, ISEG, Universidade de Lisboa, vol. 0(2), pages 81-97.
    • Handle: RePEc:pjm:journl:v:xii:y:2007:i:2:p:81-97
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