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

Water Resources Management

Volumn 29, Issue 5, 2015, Pages 1557-1574

  Sanikhani, Hadi ^a,b   Kisi, Ozgur ^c   Kiafar, Hamed ^d   Ghavidel, Sarvin Zaman Zad ^e  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

^b POLITECNICO DI MILANO   (Italy)

^c CANIK BASARI UNIVERSITY   (Turkey)

^d Ashnab Consulting Engineering Company   (Iran)

^e URMIA UNIVERSITY   (Iran)

Author keywords

Genetic programming; Grid partition; Lake level fluctuations; Neuro fuzzy; Subtractive clustering

Indexed keywords

FORECASTING; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; GENE EXPRESSION; GENETIC ALGORITHMS; GENETIC PROGRAMMING; LAKES; MEAN SQUARE ERROR;

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; COEFFICIENT OF DETERMINATION; GENE EXPRESSION PROGRAMMING; GRID PARTITION; LAKE LEVEL FLUCTUATIONS; NEURO-FUZZY; ROOT MEAN SQUARE ERRORS; SUBTRACTIVE CLUSTERING;

FUZZY INFERENCE;

EID: 84925496243     PISSN: 09204741     EISSN: 15731650     Source Type: Journal    
DOI: 10.1007/s11269-014-0894-6     Document Type: Article

References (35)

1. 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
2. Aytek A, Alp M (2008) An application of artificial intelligence for rainfall runoff modeling. J Earth Syst Sci 117(2):145–155
3. Azamathulla H, Ghani A (2011) Genetic programming for predicting longitudinal dispersion coefficients in streams. Water Resour Manag 25(6):1537–1544
4. Chang LC, Chang FJ (2001) Intelligent control for modeling of real-time reservoir operation. Hydrol Process 15:1621–1634
5. Chiu S (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2(3):267–278
6. 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
7. Cimen M, Kisi O (2009) Comparison of two different data-driven techniques in modeling lake level fluctuations in Turkey. J Hydrol 378:253–262
8. Coulibaly P (2010) Reservoir computing approach to Great Lakes water level forecasting. J Hydrol 381(1–2):76–88
9. Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129
10. Ferreira C (2006a) Gene expression programming: mathematical modeling by an artificial intelligence, 2nd edn. SpringereVerlag, Germany
11. 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
12. 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
13. Hundecha Y, Bardossy A, Theisen H (2001) Development of a fuzzy logic based rainfall-runoff model. Hydrol Sci J 46(3):363–376
14. Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685
15. 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
16. 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
17. 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
18. 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
19. Kisi O, Cimen M, Shiri J (2012a) Suspended sediment modeling using genetic programming and soft computing techniques. J Hydrol 450:48–58
20. Kisi O, Shiri J, Tombul M (2012b) Modeling rainfall-runoff process using soft computing techniques. Comput Geosci 51:108–117
21. 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
22. 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
23. Mamdani EH, Assilian S (1975) An experimental in linguistic synthesis with fuzzy logic controller. Int J Man Mach Stud 7:1–13
24. Russel SO, Campbell PF (1996) Reservoir operating rules with fuzzy programming. J Water Resour Plan Manag 122(3):165–170
25. Samhouri JF, Levin PS, Harvey CJ (2009) Quantitative evaluation of marine ecosystem indicator performance using food web models. Ecosystems 12:1283–1298
26. 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
27. 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
28. Sen Z, Kadioglu M, Batur E (2000) Stochastic modeling of the Van Lake monthly level fluctuations in Turkey. Theor Appl Climatol 65:99–110
29. 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
30. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 15:116–132
31. 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
32. 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
33. Ucan HN, Dursun S (2009) Environmental problems of Tuz Lake (Konya-Turkey). J Int Environ Appl Sci 4(2):231–233
34. Wei M, Bai B, Sung AH, Liu Q, Wang J, Cather ME (2007) Predicting injection profiles using ANFIS. Inf Sci 177:4445–4461
35. Yager RR, Filev DP (1994) Approximate clustering via the mountain method. IEEE Trans Syst Man Cybern 24(8):1279–1284