Prediction of Dam Reservoir Volume Fluctuations Using Adaptive Neuro Fuzzy Approach

Year 2017, Volume: 2 Issue: 1, 144 - 148, 25.02.2017

Fatih Unes

Abstract

Determination
of reservoir volume fluctuations is important for the operation of dam
reservoir, design of hydraulic structures, the hydropower for the energy
production, flood damage reduction, navigation in the dam reservoirs, water
quality management in reservoir and the safety of dam. In this study, reservoir
volume variations were estimated using average monthly precipitation, monthly
total volume of evaporation, dam discharge volume, and released irrigation
water amount. In the present paper, adaptive-neuro-fuzzy inference system
(ANFIS) was applied to estimating of reservoir volume fluctuations. ANFIS
results are compared with conventional multi-linear regression (MLR) model. The
results show that reservoir volume was successfully estimated using fuzzy logic
model with low mean square error and high correlation coefficients.

Keywords

Prediction

References

• [1]. J.S.R. Jang, C.T. Sun, and E. Mizutani, Neurofuzzy and Soft Computing, A Computa-tional Approach to Learning and Machine Intelligence. Prentice-Hall, New Jersey, 1997.
• [2]. E.H. Mamdani, and S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller”, International Journal of Man Machine Studies, 7, 1–13, 1975.
• [3]. T. Takagi, and M. Sugeno, “Fuzzy identification of systems and its application to modeling and control”, IEEE Transactions on System, Man and Cybernetics, 15, 116–132, 1985.
• [4]. M.E. Keskin, O. Terzi, and D. Taylan, “Fuzzy logic model approaches to daily pan evaporation estimation in Western Turkey”, Hydrological Sciences Journal, 49, 1001–1010, 2004.
• [5]. M.H. Kazeminezhad, A. Etemad-shahidi, and S.J. Mousavi, “Application of fuzzy inference system in the prediction of wave parameters”, Ocean Engineering, 32, 1709–1725, 2005.
• [6]. O. Kisi, “Daily pan evaporation modeling using a neuro-fuzzy computing technique”, Journal of Hydrology , 329, 636–646, 2006.
• [7]. O. Kisi, and O. Ozturk, “Adaptive neurofuzzy computing technique for evapo-transpiration estimation”, Journal of Irrigation and Drainage Engineering, 133, 368–379 , 2007.
• [8]. M.. Demirci, and A. Baltacı, ”Prediction of suspended sediment in river using fuzzy logic and multilinear regression approaches”, Neural Computing and Applications, 23,145-151, 2013.
• [9]. F. Üneş, “Dam reservoir level modelıng by neural network approach. A case study”, Neural Network World, 4, 461-474, 2010a.
• [10]. F. Üneş, ”Prediction of density flow plunging depth in dam reservoir: An artificial neural network approach”, Clean - Soil, Air, Water, 38, 296 – 308, 2010b.
• [11]. F. Üneş, M. Demirci, and Ö. Kişi, “Prediction of millers ferry dam reservoir level in usa using artificial neural network”, Periodica Polytechnica Civil Engineering, 59, 309–318,2015a,.
• [12]. F. Üneş, and M. Demirci, “Generalized Regression Neural Networks For Reservoir Level Modeling”, International Journal of Advanced Computational Engineering and Networking , 3, 81-84, 2015b.
• [13]. J. Shiri, and O. Kisi, “Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations”, Computers & Geosciences, 37, 1692-1701, 2011.