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River Flow Estimation and Forecasting by Using Two Different Adaptive Neuro-Fuzzy Approaches

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Abstract

This paper demonstrates the application of two different adaptive neuro-fuzzy (ANFIS) techniques for the estimation of monthly streamflows. In the first part of the study, two different ANFIS models, namely ANFIS with grid partition (ANFIS-GP) and ANFIS with sub clustering (ANFIS-SC), were used in one-month ahead streamflow forecasting and the results were evaluated. Monthly flow data from two stations, the Besiri Station on the Garzan Stream and the Baykan Station on the Bitlis Stream in the Firat-Dicle Basin of Turkey were used in the study. The effect of periodicity on the model’s forecasting performance was also investigated. In the second part of the study, the performance of the ANFIS techniques was tested for streamflow estimation using data from the nearby river. The results indicated that the performance of the ANFIS-SC model was slightly better than the ANFIS-GP model in streamflow forecasting.

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References

  • Abonyi J, Andersen H, Nagy L, Szeifert F (1999) Inverse fuzzy-process-model based direct adaptive control. Math Comput Simul 51:119–132

    Article  Google Scholar 

  • Box G, Jenkins G, Reinsel GC (1994) Time series analysis. Forecasting and control, 3rd edn. Prentice-Hall, Inc., Englewood Cliffs, NJ

  • Chang LC, Chang FJ (2001) Intelligent control for modeling of real-time reservoir operation. Hydrol Process 15:1621–1634

    Article  Google Scholar 

  • Chiu S (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2(3):267–278

    Google Scholar 

  • Chiu S (1997) Extracting fuzzy rules from data for function approximation and pattern classification. In: Dubois D, Prade H, Yager R (eds) Fuzzy information engineering: a guided tour of applications. Springer, Berlin, pp 149–162

    Google Scholar 

  • Chu HJ, Chang LC (2009) Application of optimal control and fuzzy theory for dynamic groundwater remediation design. Water Resour Manag 23(4):647–660

    Article  Google Scholar 

  • Cobaner M (2011) Evapotranspiration estimation by two different neuro-fuzzy inference systems. J Hydrol 398:292–302

    Article  Google Scholar 

  • Coulibaly P, Anctil F, Bobee B (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J Hydrol 230:244–257

    Article  Google Scholar 

  • Coulibaly P, Anctil F, Aravena R, Bobee B (2001) Artificial neural network modeling of water table depth fluctuations. Water Resour Res 37(4):885–896

    Article  Google Scholar 

  • Drake JT (2000) Communications phase synchronization using the adaptive network fuzzy inference system. Dissertation, New Mexico State University

  • El-Shafie A, Taha MR, Noureldin A (2007) A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam. Water Resour Manag 21:533–556

    Article  Google Scholar 

  • Firat M, Gungor M (2007) River flow estimation using adaptive neuro fuzzy inference system. Math Comput Simul 75:87–96

    Article  Google Scholar 

  • Goyal MK, Ojha CSP (2011) Estimation of scour downstream of a ski-jump bucket using support vector and M5 model tree. Water Resour Manag 25(9):2177–2195

    Article  Google Scholar 

  • Grino R (1992) Neural networks for univariate time series forecasting and their application to water demand prediction. Neural Netw World 5:437–445

    Google Scholar 

  • Güldal V, Tongal H (2010) Comparison of recurrent neural network, adaptive neuro-fuzzy inference system and stochastic models in Egirdir Lake level forecasting. Water Resour Manag 24(1):105–128

    Article  Google Scholar 

  • Guven A (2009) Linear genetic programming for time-series modeling of daily flow rate. J Earth Syst Sci 118(2):137–146

    Article  Google Scholar 

  • Guven A, Talu NE (2010) Gene-expression programming for estimating suspended sediment in Middle Euphrates Basin, Turkey. CLEAN-Soil Air Water 38(12):1159–1168

    Article  Google Scholar 

  • Hsu K, Gupta HV, Sorooshian S (1995) Artificial neural network modeling of the rainfall-runoff process. Water Resour Res 31(10):2517–2530

    Article  Google Scholar 

  • Hundecha Y, Bardossy A, Theisen H (2001) Development of a fuzzy logic based rainfall-runoff model. Hydrol Sci J 46(3):363–376

    Article  Google Scholar 

  • Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

    Article  Google Scholar 

  • Karunanithi N, Grenney WJ, Whitley D, Bovee K (1994) Neural networks for river flow prediction. J Comput Civil Eng ASCE 8(2):201–220

    Article  Google Scholar 

  • Kennedy P, Condon M, Dowling J (2003) Torque-ripple minimization in switched reluctant motors using a neuro-fuzzy control strategy. In: Proceeding of the IASTED International Conference on Modeling and Simulation

  • Kisi O (2005) Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrol Sci J 50(4):683–696

    Google Scholar 

  • Kisi O (2006) Daily pan evaporation modeling using a neuro-fuzzy computing technique. J Hydrol 329:636–646

    Article  Google Scholar 

  • Kisi O (2007) Evapotranspiration modeling from climate data using a neural computing technique. Hydrol Process 21(6):1925–1934

    Article  Google Scholar 

  • Kisi O (2008) River flow forecasting and estimation using different artificial neural network techniques. Hydrol Res 39(1):27–40

    Article  Google Scholar 

  • Kisi O, Haktanir T, Ardiclioglu M, Ozturk O, Yalcin E, Uludag S (2009) Adaptive neuro-fuzzy computing technique for suspended sediment estimation. Adv Eng Softw 40:438–444

    Article  Google Scholar 

  • Kisi O, Nia AM, Gosheh MG, Tajabadi MRJ, Ahmadi A (2012) Intermittent streamflow forecasting by using several data driven techniques. Water Resour Manag 26(2):457–474

    Article  Google Scholar 

  • Kumar M, Raghuwanshi NS, Singh R, Wallender WW, Pruitt WO (2002) Estimating evapotranspiration using artificial neural networks. J Irrig Drain Eng ASCE 128(4):224–233

    Article  Google Scholar 

  • Maier HR, Dandy G (2000) Neural networks for prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ Modell Softw 15(10):1–124

    Google Scholar 

  • Mamdani EH, Assilian S (1975) An experimental in linguistic synthesis with fuzzy logic controller. Int J Man Mach Stud 7:1–13

    Article  Google Scholar 

  • Ozger M, Yildirim G (2009) Determining turbulent flow friction coefficient using adaptive neuro-fuzzy computing technique. Adv Eng Softw 40:281–287

    Article  Google Scholar 

  • Salem MH, Dorrah HT (1982) Stochastic generation and forecasting models for the River Nile. International Workshop on Water Resources Planning, Alexandria

    Google Scholar 

  • Samhouri M, Abu-Ghoush M, Yaseen E, Herald T (2009) Fuzzy clustering-based modeling of surface interactions and emulsions of selected whey protein concentrate combined to ı-carrageenan and gum arabic solutions. J Food Eng 91:10–17

    Article  Google Scholar 

  • Sayed T, Tavakolie A, Razavi A (2003) Comparison of adaptive network based fuzzy inference systems and b-spline neuro-fuzzy mode choice models. J Comput Civil Eng ASCE 17(2):123–130

    Article  Google Scholar 

  • Shiri J, Kisi O (2010) Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjuction model. J Hydrol 394:486–493

    Article  Google Scholar 

  • Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 15:116–132

    Google Scholar 

  • Talebizadeh M, Moridnejad A (2011) Uncertainty analysis for the forecast of lake level fluctuations using ensembles of ANN and ANFIS models. Expert Syst Appl 38:4126–4135

    Article  Google Scholar 

  • Talei A, Chye Chua LH, Wong TSW (2010) Evaluation of rainfall and discharge inputs used by Adaptive Network-based Fuzzy Inference Systems (ANFIS) in rainfall–runoff modeling. J Hydrol 391:248–262

    Article  Google Scholar 

  • Tsukamoto Y (1979) An approach to reasoning method. In: Gupta M, Ragade RK, Yager RR (eds) Advances in fuzzy set theory and applications. Elsevier, Amsterdam, pp 137–149

  • Wei M, Bai B, Sung AH, Liu Q, Wang J, Cather ME (2007) Predicting injection profiles using ANFIS. Inform Sci 177:4445–4461

    Article  Google Scholar 

  • Wood EF (1980) Real time forecasting control of water resource systems. Workshop Report, Pergamon Press, New York

    Google Scholar 

  • Yager RR, Filev DP (1994) Approximate clustering via the mountain method. IEEE Trans Syst Man Cybern 24(8):1279–1284

    Article  Google Scholar 

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Authors and Affiliations

  1. Young Researchers Club, Tabriz Branch, Islamic Azad University, Tabriz, Iran

    Hadi Sanikhani

  2. Civil Engineering Department, Canik Basari University, Samsun, Turkey

    Ozgur Kisi

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  1. Hadi Sanikhani
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  2. Ozgur Kisi
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Correspondence to Hadi Sanikhani.

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Sanikhani, H., Kisi, O. River Flow Estimation and Forecasting by Using Two Different Adaptive Neuro-Fuzzy Approaches. Water Resour Manage 26, 1715–1729 (2012). https://doi.org/10.1007/s11269-012-9982-7

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  • DOI: https://doi.org/10.1007/s11269-012-9982-7

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