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Water 2013, 5(3), 1441-1456; doi:10.3390/w5031441

Computing Air Demand Using the Takagi–Sugeno Model for Dam Outlets

Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
Civil Engineering Research Group, The University of Salford, School of Computing, Science and Engineering, Newton Building, Salford, Greater Manchester M5 4WT, UK
Author to whom correspondence should be addressed.
Received: 12 July 2013 / Revised: 13 August 2013 / Accepted: 13 September 2013 / Published: 23 September 2013
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An adaptive neuro-fuzzy inference system (ANFIS) was developed using the subtractive clustering technique to study the air demand in low-level outlet works. The ANFIS model was employed to calculate vent air discharge in different gate openings for an embankment dam. A hybrid learning algorithm obtained from combining back-propagation and least square estimate was adopted to identify linear and non-linear parameters in the ANFIS model. Empirical relationships based on the experimental information obtained from physical models were applied to 108 experimental data points to obtain more reliable evaluations. The feed-forward Levenberg-Marquardt neural network (LMNN) and multiple linear regression (MLR) models were also built using the same data to compare model performances with each other. The results indicated that the fuzzy rule-based model performed better than the LMNN and MLR models, in terms of the simulation performance criteria established, as the root mean square error, the Nash–Sutcliffe efficiency, the correlation coefficient and the Bias. View Full-Text
Keywords: dam; fuzzy model; outlet works; reservoir; subtractive clustering; Takagi-Sugeno; vent air discharge dam; fuzzy model; outlet works; reservoir; subtractive clustering; Takagi-Sugeno; vent air discharge

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Zounemat-Kermani, M.; Scholz, M. Computing Air Demand Using the Takagi–Sugeno Model for Dam Outlets. Water 2013, 5, 1441-1456.

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