Prediction of dissolved oxygen in reservoirs using adaptive network-based fuzzy inference system

Journal of Hydroinformatics

Volumn 14, Issue 1, 2012, Pages 167-179

  Ranković, Vesna ^a   Radulović, Jasna ^a   Radojević, Ivana ^a   Ostojić, Aleksandar ^a   Čomić, Ljiljana ^a  

^a UNIVERSITY OF KRAGUJEVAC   (Serbia)

Author keywords

Adaptive network based fuzzy inference system; Dissolved oxygen; Modelling; Reservoir

Indexed keywords

EID: 84865022391     PISSN: 14647141     EISSN: None     Source Type: Journal    
DOI: 10.2166/hydro.2011.084     Document Type: Article

References (38)

1. Altunkaynak, A., Özger, M. & Cakmakci, M. 2005 Fuzzy logic modeling of the dissolved oxygen fluctuations in Golden Horn. Ecol Model 189 (3-4), 436-446. (Pubitemid 41630728)
2. Antonopoulos, V. Z. & Gianniou, S. K. 2003 Simulation of water temperature and dissolved oxygen distribution in the Lake Vegoritis, Greece. Ecol. Model. 160 (1-2), 39-53. (Pubitemid 36117245)
3. Chaves, P. & Kojiri, T. 2007 Conceptual fuzzy neural network model for water quality simulation. Hydrol. Process. 21 (5), 634-646. (Pubitemid 46371803)
4. Chen, Q., Morales-Chaves, Y., Li, H. & Mynett, A. E. 2006 Hydroinformatics techniques in eco-environmental modelling and management. J. Hydroinform. 8 (4), 297-316.
5. Chen, D., Lu, J. & Shen, Y. 2010 Artificial neural network modelling of concentrations of nitrogen, phosphorus and dissolved oxygen in a non-point source polluted river in Zhejiang Province, southeast China. Hydrol. Process. 24 (3), 290-299.
6. Chon, T. S. & Park, Y. S. 2006 Ecological informatics as an advanced interdisciplinary interpretation of ecosystems. Ecol. Inform. 1 (3), 213-217.
7. LjComic & A. Ostojic (eds). 2005 The Gruza Reservoir (in Serbian). Faculty of Science, Kragujevac, Serbia and Montenegro.
8. Dogan, E., Sengorur, B. & Koklu, R. 2009 Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique. J. Environ. Manag. 90 (2), 1229-1235.
9. Evsukoff, A., Branco, A. C. S. & Galichet, S. 2002 Structure identification and parameter optimization for non-linear fuzzy modeling. Fuzzy Sets Sys. 132 (2), 173-188. (Pubitemid 35290970)
10. Firat, M. & Güngör, M. 2008 Hydrological time-series modelling using an adaptive neuro-fuzzy inference system. Hydrol. Process. 22 (13), 2122-2132. (Pubitemid 351927456)
11. Giusti, E. & Marsili-Libelli, S. 2009 Spatio-temporal dissolved oxygen dynamics in the Orbetello lagoon by fuzzy pattern recognition. Ecol. Model. 220 (19), 2415-2426.
12. Jacquin, A. P. & Shamseldin, A. Y. 2009 Review of the application of fuzzy inference systems in river flow forecasting. J. Hydroinform. 11 (3-4), 202-210.
13. Jang, J. S. R. 1993 ANFIS: Adaptive-Network-Based Fuzzy Inference Systems. IEEE Trans. Syst. Man Cybernet. 23 (3), 665-685.
14. Jang, J. S. R. 1996 Input selection for ANFIS learning. In Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, New Orleans, LA, September (IEEE Neural Networks Council, ed.), pp. 1493-1499.
15. Jang, J. S. R. & Sun, C. T. 1993 Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE Trans. Neur. Net. 4 (1), 156-159.
16. Jang, J. S. R., Sun, C. T. & Mizutani, E. 1997 Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice-Hall, Upper Saddle River, NJ.
17. Kosko, B. 1994 Fuzzy Systems as universal approximators. IEEE Trans. Comp. 43 (11), 1329-1333.
18. Kuo, J. T., Hsieh, M. H., Lung, W. S. & She, N. 2007 Using artificial neural network for reservoir eutrophication prediction. Ecol. Model. 200 (1-2), 171-177. (Pubitemid 44828246)
19. Lin, J. Y., Cheng, C. T. & Chau, K. W. 2006 Using support vector machines for long-term discharge prediction. Hydrol. Sci. J. 51 (4), 599-612.
20. Liou, S. M., Lo, S. L. & Hu, C. Y. 2003 Application of two-stage fuzzy set theory to river quality evaluation in Taiwan. Water Res. 37 (6), 1406-1416.
21. Marsili-Libelli, S. 2004 Fuzzy prediction of the algal blooms in the Orbetello lagoon. Environ. Model. Softw. 19 (9), 799-808. (Pubitemid 38722588)
22. May, R. J., Maier, H. R., Dandy, G. C. & Fernando, T. M. K. G 2008 Non-linear variable selection for artificial neural networks using partial mutual information. Environ. Model. Software 23 (10-11), 1312-1326.
23. Muttil, N. & Chau, K. W. 2006 Neural network and genetic programming for modelling coastal algal blooms. Int. J. Environ. Pollut. 28 (3-4), 223-238. (Pubitemid 44905987)
24. Ostojic, A., Curcic, S., Comic, Lj. & Topuzovic, M. 2005 Estimate of the eutrophication process in the Gruza Reservoir (Serbia and Montenegro). Acta Hydrochim. Hydrobiol. 33 (6), 605-613. (Pubitemid 43033946)
25. Palani, S., Liong, S. Y. & Tkalich, P. 2008 An ANN application for water quality forecasting. Marine Pollut. Bull. 56 (9), 1586-1597.
26. Pereira, G. C., Evsukoff, A. & Ebecken, N. F. F. 2009 Fuzzy modelling of chlorophyll production in a Brazilian upwelling system. Ecol. Model. 220 (12), 1506-1512.
27. Rankovic, V., Radulovic, J., Radojevic, I., Ostojic, A. & Comic, Lj. 2010 Neural network modeling of dissolved oxygen in the Gruza reservoir, Serbia. Ecol. Model. 221 (8), 1239-1244.
28. Sengorur, B., Dogan, E., Koklu, R. & Samandar, A. 2006 Dissolved oxygen estimation using artificial neural network for water quality control. Fresen. Environ. Bull. 15 (9a), 1064-1067. (Pubitemid 44555334)
29. Singh, K. P., Basant, A., Malik, A. & Jain, G. 2009 Artificial neural network modeling of the river water quality - a case study. Ecol Model. 220 (6), 888-895.
30. Soyupak, S. & Chen, D. G. 2004 Fuzzy logic model to estimate seasonal pseudo steady state chlorophyll-a concentrations in reservoirs. Environ. Model. Assess. 9 (1), 51-59. (Pubitemid 39008977)
31. Soyupak, S., Karaer, F., Gürbüz, H., Kivrak, E., Sentürk, E. & Yazici, A. 2003 A neural network-based approach for calculating dissolved oxygen profiles in reservoirs. Neural Comput. Appl. 12 (3-4), 166-172. (Pubitemid 38039903)
32. Sugeno, M. & Kang, G. T. 1988 Structure identification of fuzzy model. Fuzzy Sets Syst. 28 (1), 15-33.
33. Takagi, T. & Sugeno, M. 1985 Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 15 (1), 116-132.
34. Vandenberghe, V., Bauwens, W. & Vanrolleghem, P. A. 2007 Evaluation of uncertainty propagation into river water quality predictions to guide future monitoring campaigns. Environ. Model. Software 22 (5), 725-732. (Pubitemid 44970006)
35. Wang, H., Kwong, S., Jinb, Y., Wei, W. & Mand, K. F. 2005 Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction. Fuzzy Set. Syst. 149 (1), 149-186.
36. Wang, W. C., Chau, K. W., Cheng, C. T. & Qiu, L. 2009 A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J. Hydrol. 374 (3-4), 294-306.
37. Wieland, R. & Mirschel, W. 2008 Adaptive fuzzy modeling versus artificial neural networks. Environ. Model. Software 23 (2), 215-224. (Pubitemid 47488938)
38. Ying, Z., Jun, N., Fuyi, C. & Liang, G. 2007 Water quality forecast through application of BP neural network at Yuquio reservoir. J. Zhejiang Univ. Sci. A. 8 (9), 1482-1487.