Research Article
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Prediction of Dam Reservoir Volume Fluctuations Using Adaptive Neuro Fuzzy Approach

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

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.

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

Abstract

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.
There are 13 citations in total.

Details

Journal Section Makaleler
Authors

Fatih Unes

Publication Date February 25, 2017
Published in Issue Year 2017 Volume: 2 Issue: 1

Cite

APA Unes, F. (2017). Prediction of Dam Reservoir Volume Fluctuations Using Adaptive Neuro Fuzzy Approach. European Journal of Engineering and Natural Sciences, 2(1), 144-148.
AMA Unes F. Prediction of Dam Reservoir Volume Fluctuations Using Adaptive Neuro Fuzzy Approach. European Journal of Engineering and Natural Sciences. February 2017;2(1):144-148.
Chicago Unes, Fatih. “Prediction of Dam Reservoir Volume Fluctuations Using Adaptive Neuro Fuzzy Approach”. European Journal of Engineering and Natural Sciences 2, no. 1 (February 2017): 144-48.
EndNote Unes F (February 1, 2017) Prediction of Dam Reservoir Volume Fluctuations Using Adaptive Neuro Fuzzy Approach. European Journal of Engineering and Natural Sciences 2 1 144–148.
IEEE F. Unes, “Prediction of Dam Reservoir Volume Fluctuations Using Adaptive Neuro Fuzzy Approach”, European Journal of Engineering and Natural Sciences, vol. 2, no. 1, pp. 144–148, 2017.
ISNAD Unes, Fatih. “Prediction of Dam Reservoir Volume Fluctuations Using Adaptive Neuro Fuzzy Approach”. European Journal of Engineering and Natural Sciences 2/1 (February 2017), 144-148.
JAMA Unes F. Prediction of Dam Reservoir Volume Fluctuations Using Adaptive Neuro Fuzzy Approach. European Journal of Engineering and Natural Sciences. 2017;2:144–148.
MLA Unes, Fatih. “Prediction of Dam Reservoir Volume Fluctuations Using Adaptive Neuro Fuzzy Approach”. European Journal of Engineering and Natural Sciences, vol. 2, no. 1, 2017, pp. 144-8.
Vancouver Unes F. Prediction of Dam Reservoir Volume Fluctuations Using Adaptive Neuro Fuzzy Approach. European Journal of Engineering and Natural Sciences. 2017;2(1):144-8.