Abstract
A major fortress for flood and draughts is a reservoir. Capacity prediction of a reservoir is an integral part for its modelling. This paper exhibits the use of soft-computing technique ANFIS (Adaptive Neuro Fuzzy Inference System) to model behavior of Ukai reservoir. Using input/output data values, the proposed ANFIS computed balance capacity of Ukai reservoir. The input parameters include storage capacity for power generation, releases in left bank main canal, releases through gates, evaporation and inflow in to the reservoir. Out of the 14 models generated, the minimum error obtained to calculate the balance capacity of the reservoir was 0.06675. Calibration has been done for the years 2004–2010. The membership function used in this case was triangular with an epoch of 25. Model is validated for 2011–2014 and the minimum error predicted for validating the model was 0.0592, which is accurate enough to predict the capacity of the reservoir at the end of the period.
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Saxena, S., Yadav, S.M. (2017). Neuro Fuzzy Application in Capacity Prediction and Forecasting Model for Ukai Reservoir. In: Garg, V., Singh, V., Raj, V. (eds) Development of Water Resources in India. Water Science and Technology Library, vol 75. Springer, Cham. https://doi.org/10.1007/978-3-319-55125-8_9
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DOI: https://doi.org/10.1007/978-3-319-55125-8_9
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