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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arnold, J.G., Srinivasan, R., Muttiah, R.S., Williams, J.R.: Large area hydrological modelling and assessment Part I. J. Am. Water Resour. Assoc. 73–89 (1998)
Bijaya, P., Shreshtha, L.D., Stakhiv, E.Z.: Fuzzy rule based modelling of reservoir operation. J. Water Resour. Plan. Manag. 262–269 (1996)
Bisht, D., Jain, S., Mohan Raju, M.: Prediction of water table elevation fluctuation through fuzzy logic and artificial neural networks. Int. J. Adv. Sci. Technol. 51 (2013)
Jang, J.S.R.: ANFIS (Adaptive network based fuzzy inference system). IEEE Trans. Syst. Man Cybern. 23, 665–685 (1993)
Kadhim, H.H.: Self learning of ANFIS inverse control using iterative learning technique. Int. J. Comput. Appl. 21, 24–29 (2011)
Kerachian, R., Karamouz, M., Soltany, F.: Optimal reservoir operation considering the water quality issues. Application of adaptive neuro fuzzy inference system. In: World Environmental and Water Resource Congress ASCE (2006)
Raman, H., Chandramouli, V.: Deriving a general operating policy for reservoirs using neural network. J. Water Resour. Plan. Manag. 122(4), 262–269 (1996)
Roy, S.S.: Design of adaptive neuro fuzzy inference system for predicting surface roughness in turning operation. J. Sci. Ind. Res. 64 (2005)
Takagi, T., Sugeno, M.: Fuzzy identification systems and its application to modelling and control. IEEE Trans. Syst. Man Cybern. 15, 116–132 (1985)
Talei, A., Chua, L.H., Quek, C., Jansson, P.E.: Runoff forecasting using a Takagi-Sugeno neuro fuzzy model with online learning. J. Hydrol. 17–32 (1985)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-55125-8_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55124-1
Online ISBN: 978-3-319-55125-8
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)