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Comparison of Different Data-Driven Approaches for Modeling Lake Level Fluctuations: The Case of Manyas and Tuz Lakes (Turkey)

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Abstract

Modeling lake level fluctuation is very essential for planning and design of hydraulic structures along the lake coasts. In this study, namely two different adaptive-neuro-fuzzy inference system (ANFIS) including ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC), and gene expression programming (GEP) were applied to forecast 1-, 2- and 3-month ahead lake-level fluctuations of Manyas and Tuz, Turkey. Comparison of the models indicated that the optimal ANFIS-GP models performed better than the optimal ANFIS-SC and GEP models in forecasting 1- and 3-month ahead lake levels while the ANFIS-SC model showed better accuracy than the other models in 2-month ahead forecasting. The ANFIS-GP model comprising lake level values of current and one previous months successfully forecasted 1-month ahead lake level with root mean square error (RMSE) of 0.251 and coefficient of determination (R2) of 0.872. For the Tuz Lake, the optimal ANFIS-SC models were found to be better than the optimal ANFIS-GP and GEP models for forecasting 1- and 2-month ahead lake levels while the GEP model performed better than the other models in and 3-month ahead lake level forecasting. The ANFIS-SC model comprising lake level values of current and three previous months successfully forecasted 1-month ahead lake level with RMSE of 0.120 and R2 of 0.724. Based on the comparisons, it was found that the GEP, ANFIS-GP and ANFIS-SC models could be successfully employed in forecasting lake level fluctuations.

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References

  • Altunkaynak A, Şen Z (2007) Fuzzy logic model of lake water level fluctuations in Lake Van, Turkey. Theor Appl Climatol 90(3–4):227–233

    Article  Google Scholar 

  • Aytek A, Alp M (2008) An application of artificial intelligence for rainfall runoff modeling. J Earth Syst Sci 117(2):145–155

    Article  Google Scholar 

  • Azamathulla H, Ghani A (2011) Genetic programming for predicting longitudinal dispersion coefficients in streams. Water Resour Manag 25(6):1537–1544

  • 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 

  • Cimen M, Kisi O (2009) Comparison of two different data-driven techniques in modeling lake level fluctuations in Turkey. J Hydrol 378:253–262

    Article  Google Scholar 

  • Coulibaly P (2010) Reservoir computing approach to Great Lakes water level forecasting. J Hydrol 381(1–2):76–88

    Article  Google Scholar 

  • Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129

    Google Scholar 

  • Ferreira C (2006a) Gene expression programming: mathematical modeling by an artificial intelligence, 2nd edn. SpringereVerlag, Germany

    Google Scholar 

  • Ferreira C (2006b) Automatically defined functions in gene expression programming. In: Nedjah N, de M. Mourelle L, Abraham A (eds) Genetic systems programming: Theory and experiences, studies in computational intelligence, vol 13. Springer-Verlag, pp 21–56

  • Guldal V, Tongal H (2009) Comparison of recurrent neural network, adaptive neuro-fuzzy inference system and stochastic models in Eğirdir Lake level forecasting. Water Resour Manag 24(1):105–128

    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 

  • Jones RN, McMahon TA, Bowler JM (2001) Modeling historical lake levels and recent climate change at three closed lakes, Western Victoria, Australia (c. 1840–1990). J Hydrol 246(1–4):159–180

    Article  Google Scholar 

  • Karafistan A, Arik-Colakoglu F (2005) Physical, chemical and microbiological water quality of the Manyas Lake, Turkey. Mitig Adapt Strateg Glob Chang 10:127–143

    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

  • Keskin ME, Terzi O, Taylan D (2004) Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey. Hydrol Sci J 49(6):1001–1010

    Article  Google Scholar 

  • Kisi O, Cimen M, Shiri J (2012a) Suspended sediment modeling using genetic programming and soft computing techniques. J Hydrol 450:48–58

    Article  Google Scholar 

  • Kisi O, Shiri J, Tombul M (2012b) Modeling rainfall-runoff process using soft computing techniques. Comput Geosci 51:108–117

    Article  Google Scholar 

  • Kurtulus B, Razack M (2010) Modeling daily discharge responses of a large karstic aquifer using soft computing methods: artificial neural network and neuro-fuzzy. J Hydrol 381(1):101–111

    Article  Google Scholar 

  • Leroy S, Kazanci N, Ileri Ö, Kibar M, Emre O, McGee E, Griffiths HI (2002) Abrupt environmental changes within late Halocene lacustrine sequence south of the Marmara Sea (Lake Manyas, N-W Turkey): possible links with seismic events. Mar Geol 190:531–552

    Article  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 

  • Russel SO, Campbell PF (1996) Reservoir operating rules with fuzzy programming. J Water Resour Plan Manag 122(3):165–170

    Article  Google Scholar 

  • Samhouri JF, Levin PS, Harvey CJ (2009) Quantitative evaluation of marine ecosystem indicator performance using food web models. Ecosystems 12:1283–1298

  • Sanikhani H, Kisi O, Nikpour MR, Dinpashoh Y (2012) Estimation of daily pan evaporation using two different adaptive neuro-fuzzy computing techniques. Water Resour Manag 26:4347–4365

    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 

  • Sen Z, Kadioglu M, Batur E (2000) Stochastic modeling of the Van Lake monthly level fluctuations in Turkey. Theor Appl Climatol 65:99–110

    Article  Google Scholar 

  • Shu C, Ouarda TBMJ (2008) Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system. J Hydrol 349(1):31–43

    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

    Article  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 

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

  • Ucan HN, Dursun S (2009) Environmental problems of Tuz Lake (Konya-Turkey). J Int Environ Appl Sci 4(2):231–233

    Google Scholar 

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

  • 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 and Elite Club, Saveh Branch, Islamic Azad University, Saveh, Iran

    Hadi Sanikhani

  2. Department of Hydraulic, Environmental and Surveying Engineering, Politecnico de Milano, Milan, Italy

    Hadi Sanikhani

  3. Architecture and Engineering Faculty, Civil Engineering Department, Canik Basari University, Samsun, Turkey

    Ozgur Kisi

  4. Ashnab Consulting Engineering Company, Tehran, Iran

    Hamed Kiafar

  5. Water Engineering Department, Faculty of Agriculture, Urmia University, Urmia, Iran

    Sarvin Zaman Zad Ghavidel

Authors
  1. Hadi Sanikhani
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  2. Ozgur Kisi
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  3. Hamed Kiafar
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  4. Sarvin Zaman Zad Ghavidel
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Correspondence to Hadi Sanikhani.

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Sanikhani, H., Kisi, O., Kiafar, H. et al. Comparison of Different Data-Driven Approaches for Modeling Lake Level Fluctuations: The Case of Manyas and Tuz Lakes (Turkey). Water Resour Manage 29, 1557–1574 (2015). https://doi.org/10.1007/s11269-014-0894-6

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  • DOI: https://doi.org/10.1007/s11269-014-0894-6

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