Comparison of fuzzy inference systems for streamflow prediction

Hydrological Sciences Journal

Volumn 54, Issue 2, 2009, Pages 261-273

  Özger, Mehmet ^a  

^a ISTANBUL TECHNICAL UNIVERSITY   (Turkey)

Author keywords

Euphrates River; Fuzzy logic; Genetic algorithms; Mamdani; Prediction; Streamflow; Takagi Sugeno

Indexed keywords

DEFUZZIFICATION METHOD; EUPHRATES RIVER; EXOGENOUS VARIABLES; FLOW PREDICTION; FUZZY INFERENCE SYSTEMS; FUZZY MEMBERSHIP FUNCTION; LINEAR FUNCTIONS; LINGUISTIC EXPRESSIONS; MAMDANI; MAMDANI TYPE; PREDICTION; STREAMFLOW FORECASTING; STREAMFLOW PREDICTION; SUB-SETS; T-S FUZZY; TAKAGI-SUGENO;

FUZZY RULES; FUZZY SYSTEMS; GENETIC ALGORITHMS; MEMBERSHIP FUNCTIONS; RIVERS; STREAM FLOW;

FUZZY INFERENCE;

FUZZY MATHEMATICS; GENETIC ALGORITHM; NUMERICAL MODEL; PREDICTION; STREAMFLOW;

EUPHRATES RIVER; EURASIA;

EID: 69949181687     PISSN: 02626667     EISSN: None     Source Type: Journal    
DOI: 10.1623/hysj.54.2.261     Document Type: Article

References (46)

1. Abebe, A. J., Solomatine, D. P. & Venneker, R. G. W. (2000) Application of adaptive fuzzy rule-based models for reconstruction of missing precipitation events. Hydrol. Sci. J. 45(3), 425-436.
2. Altunkaynak, A., Özger, M. & Çakmakci, M. (2005a) Water consumption prediction of Istanbul city by using fuzzy logic approach. Water Resour. Manage. 19(5), 641-654.
3. Altunkaynak, A., Özger, M. & Çakmakci, M. (2005b) Fuzzy logic modelling of the dissolved oxygen fluctuations in Golden Horn. Ecol. Model. 189 (3-4), 436-446.
4. Alvisi, S., Mascellani, G., Franchini, M. & Bárdossy, A. (2006) Water level forecasting through fuzzy logic and artificial neural network approaches. Hydrol. Earth System Sci. 10, 1-17.
5. Bárdossy, A., Mascellani, G. & Franchini, M. (2006) Fuzzy unit hydrograph. Water Resour. Res. 42, W02401. doi:10.1029/ 2004WR003751.
6. Campolo, M., Adreussi, P. & Soldati, A. (1999) River flood forecasting with a neural network. Water Resour. Res. 35, 1191-1197.
7. Campolo, M., Soldati A. & Andreussi, P. (2003) Artificial neural network approach to flood forecasting in the River Arno. Hydrol. Sci. J. 48, 381-398.
8. Chang, F. J. & Chang, Y. T. (2006) Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv. Water Res. 29(1), 1-10.
9. Chang, L. C. & Chang, F. J. (2001) Intelligent control for modelling of real time reservoir operation. Hydrol. Processes 15, 1621-1634.
10. Chen, S. H., Lin, Y. H., Chang, L. C & Chang, F. J. (2006) The strategy of building a flood forecast model by neuro-fuzzy network. Hydrol. Process. 20, 1525-1540.
11. Chiu, S. L. (1994) Fuzzy model identification based on cluster estimation. J. Intelligent and Fuzzy Systems 2, 267-278
12. Deka, P. & Chandramouli, V. (2005) Fuzzy neural network model for river flow prediction. J. Hydrol. Engng 10(4), 302-314.
13. Firat, M. & Güngör, M. (2007) River flow estimation using adaptive neuro-fuzzy inference system. Mathematics and Computers in Simulation 75(3-4), 87-96.
14. Firat, M. & Güngör, M. (2007) Hydrological time-series modeling using an adaptive neuro-fuzzy inference system. Hydrol. Processes. doi:10.1002/hyp.6812.
15. Goldberg, D. E. (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Kluwer Academic Publishers, Boston, Massachusetts, USA.
16. Goswami, M., O'Connor, K. M., Bhattarai, K. P. & Shamseldin, A. Y. (2005) Assessing the performance of eight real-time updating models and procedures for the Brosna River. Hydrol. Earth Syst. Sci. 9, 394-411.
17. Gustafson, D. & Kessel, W. (1979) Fuzzy clustering with a fuzzy covariance matrix. In: Proc. IEEE CDC (San Diego, California, USA), 761-766.
18. Hasebe, M. & Nagayama, Y. (2002) Reservoir operation using the neural network and fuzzy systems for dam control and operation support. Adv. Engng Software 33(5), 245-260.
19. Holland, J. (1975) Adoption in Neural and Artificial Systems. University of Michigan Press, Ann Arbor, Michigan, USA.
20. Hong, Y. S., Rosen, M. R. & Reeves, R. R. (2002) Dynamic fuzzy modeling of storm water infiltration in urban fractured aquifers. J. Hydrol. Engng 7(5), 380-391.
21. Hundecha, Y., Bárdossy, A. & Theisen, H.-W. (2001) Development of a fuzzy logic-based rainfall - runoff model. Hydrol. Sci. J. 46(3), 363-376.
22. Imrie, C. E., Durucan, S. & Korre, A. (2000) River flow prediction using artificial neural networks: generalisation beyond the calibration range. J. Hydrol. 233, 138-153.
23. Jacquin, A. P. & Shamseldin, A. Y. (2006) Development of rainfall - runoff models using Takagi-Sugeno fuzzy inference systems. J. Hydrol. 329, 154-173.
24. Jain, S. K., Das, A. & Srivastava, D. K. (1999) Application of ANN for reservoir inflow prediction and operation. J. Water Resour Plan. Manage. 125(5), 263-271.
25. Jang, J. S. R. (1993) ANFIS: Adaptive network based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665-684.
26. Koutsoyiannis, D. (2005a) Uncertainty, entropy, scaling and hydrological stochastics, 1. Marginal distributional properties of hydrological processes and state scaling. Hydrol. Sci. J. 50(3), 381-404.
27. Koutsoyiannis, D. (2005b) Uncertainty, entropy, scaling and hydrological stochastics, 2. Time dependence of hydrological processes and time scaling. Hydrol. Sci. J. 50(3), 405-426.
28. Koutsoyiannis, D., Yao, H & Georgakakos, A. (2008) Medium-range flow prediction for the Nile: a comparison of stochastic and deterministic methods. Hydrol. Sci. J. 53(1), 142-164.
29. Lallahem, S. & Mania, J. (2003) Evaluation and forecasting of daily groundwater outflow in a small chalky watershed. Hydrol Processes 17(8), 1561-77.
30. Mamdani, E. H. (1974) Application of fuzzy algorithms for simple dynamic plant. Proc. IEE 121, 1585-1588.
31. Montanari, A., Rosso, R. & Taqqu, M. S. (2000) A seasonal fractional ARIMA model applied to the Nile River monthly flows at Aswan. Water Resour. Res. 36(5), 1249-1259.
32. Nayak, P. C., Sudheer, K. P., Rangan, D. M. & Ramasastri, K. S. (2004) A neuro-fuzzy computing technique for modelling hydrological time series. J. Hydrol. 291, 52-66.
33. Özger, M. & Şen, Z. (2007) Prediction of wave parameters by using fuzzy logic approach. Ocean Engineering, 34(3-4), 460-469.
34. Ross, J. T. (1995) Fuzzy Logic with Engineering Applications. McGraw-Hill Inc, New York, USA.
35. See, L. & Openshaw, S. (2000) A hybrid multi-model approach to river level forecasting. Hydrol. Sci. J. 45(4), 523-536.
36. Shamseldin, A. Y. (1997) Application of a neural network technique to rainfall - runoff modeling. J. Hydrol. 199, 272-294.
37. Şen, Z. (2004) Fuzzy Logic and System Models in Water Sciences. Turkish Water Foundation, Istanbul, Turkey.
38. Takagi, T. & Sugeno, M. (1985) Fuzzy identification of systems and its application to modeling and control. IEEE Trans. Syst. Man. Cybern. 15, 116-132.
39. Thirumalaiah, K. & Deo, M. C. (1998) River stage forecasting using artificial neural networks. J. Hydrol. Eng. 3(1), 26-32.
40. Tokar, A. S. & Markus, M. (2000) Precipitation - runoff modelling using artificial neural networks and conceptual models. J. Hydrol. Engng 5(2), 156-161.
41. Tütmez, B. & Tercan, A. E. (2007) Spatial estimation of some mechanical properties of rocks by fuzzy modeling. Computers and Geotechnics 34, 10-18.
42. Vernieuwe H., Georgieva, O., De Baets, B., Pauwels, V. R. N., Verhoest, N. E. C. & De Troch, F. P. (2005) Comparison of data-driven Takagi-Sugeno models of rainfall - discharge dynamics. J. Hydrol. 302(1-4), 173-186.
43. Xiong, L., Shamseldin, A. Y. & O'Connor, K. M. (2001) A non-linear combination of the forecasts of rainfall - runoff models by the first-order Takagi-Sugeno fuzzy system. J. Hydrol. 245, 196-217.
44. Yang, C. C., Prasher, S. O., Lacroix, R., Sreekanth, S., Patni, N. K. & Masse, L. (1997) Artificial neural network model for subsurfacedrained farmland. J. Irrig. Drain. Engng 123, 285-292.
45. Ying, H., Ding, Y., Li, S. & Shao, S. (1999) Comparison of necessary conditions for typical Takagi-Sugeno and Mamdani fuzzy systems as universal approximators. IEEE Trans. Systems, Man and Cybernetics Part A: Systems and Humans 29(5), 508-514.
46. Zadeh, L. A. (1965) Fuzzy sets. Information and Control 8(2), 338-353.