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Fuzzy Model Identification Based on Cluster Estimation for Reservoir Inflow Forecasting by P. C. Nayak; K. P. Sudheer is a Environmental Science article available to read on EtoBox.

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## Abstract Fuzzy theory appears to be extremely effective at handling dynamic, non‐linear and noisy data, especially when the underlying physical relationships are not fully understood. Since hydrologists are still uncertain about many of the aspects of the physical processes in the watershed, fuzzy theory has proved to be a very attractive tool enabling them to investigate such problems. The effectiveness of the fuzzy model lies in the identification of the antecedent membership function (MF), which is generally addressed through a fuzzy clustering approach. Most of the applications of fuzzy computing in hydrology seem to have selected the clustering algorithm quite arbitrarily. However, it is apparent that, as the antecedent parameters are based solely on the identified clusters, the method used for clustering should certainly have an impact on the overall performance of the model. This paper presents the results of a study conducted to investigate the impact of choice of clustering algorithm on the overall performance of a fuzzy‐based hydrologic model. The research is illustrated through a case study of developing a Takagi–Sugeno fuzzy model for reservoir inflow forecasting in

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Author

P. C. Nayak; K. P. Sudheer

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 0885-6087)

Published

2007

Language

EN

Field

Environmental Science (Physical Sciences)