Use of neurofuzzy networks to improve wastewater flow-rate forecasting

Environmental Modelling and Software

Volumn 24, Issue 6, 2009, Pages 686-693

  Fernandez, F J ^a   Seco, A ^b   Ferrer, J ^c   Rodrigo, M A ^a  

^a UNIVERSITY OF CASTILLA LA MANCHA   (Spain)

^b UNIVERSITY OF VALENCIA   (Spain)

^c UNIVERSITAT POLITÈCNICA DE VALÈNCIA   (Spain)

Author keywords

Flow rates; Foresight; Neurofuzzy network; Predictive model based control; Time series; Urban wastewater

Indexed keywords

BIOCHEMICAL OXYGEN DEMAND; FORECASTING; MODEL PREDICTIVE CONTROL; NEURAL NETWORKS; PREDICTIVE CONTROL SYSTEMS; TIME SERIES; WASTEWATER; WASTEWATER RECLAMATION;

FLOW-RATES; FORESIGHT; NEUROFUZZY NETWORK; PREDICTIVE MODEL-BASED CONTROL; URBAN WASTEWATER;

WASTEWATER TREATMENT;

ALGORITHM; ARTIFICIAL INTELLIGENCE; ERROR ANALYSIS; FLOW MEASUREMENT; FORECASTING METHOD; FUZZY MATHEMATICS; WASTEWATER;

EID: 60649120410     PISSN: 13648152     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.envsoft.2008.10.010     Document Type: Article

References (25)

1. Al-Alawi S., Abdul-Wahab S., and Bakheit C. Combining principal component regression and artificial neural networks for more accurate predictions of ground-level ozone. Environmental Modelling & Software 23 4 (2008) 396-403
2. Amari S., Murata N., Müller K.-R., Finke M., and Yang H. Asymptotic statistical theory of overtraining and cross-validation. IEEE Transactions on Neural Networks 8 5 (1997) 985-996
3. Barreto-Neto A., and de Souza Filho C. Application of fuzzy logic to the evaluation of runoff in a tropical watershed. Environmental Modelling & Software 23 2 (2008) 244-253
4. Enbutsu I., Baba K., Hara N., Waseda K., and Nogita S. Integration of multi AI paradigms for intelligent operation support systems - fuzzy rule extraction from a neural network. Water Science and Technology 28 11-12 (1993) 333-340
5. Gobi A.F., and Pedrycz W. The potential of fuzzy neural networks in the realization of approximate reasoning engines. Fuzzy Sets and Systems 157 (2006) 2954-2973
6. Hiraga I., Furuhashi T., and Uchikawa Y. An acquisition of operator's rules for collision avoidance using fuzzy neural networks. IEEE Transactions on Fuzzy Systems 3 3 (1995) 281-287
7. Hill T., and Lewicki P. STATISTICS Methods and Applications (2007), StatSoft, Tulsa, OK pp. 504-510
8. Hornik K., Stinchcombe M., and White H. Multilayer feedforward networks as universal aproximators. Neural Networks 2 (1989) 359-366
9. Jakeman A.J., Letcher R.A., and Norton J.P. Ten iterative steps in development and evaluation of environmental models. Environmental Modelling & Software 21 (2006) 602-614
10. Kosko, B. 1992. Fuzzy systems as universal approximators. In: Proceedings of the IEEE International Conference Fuzzy Systems, pp. 1153-1162.
11. Koutroumanidis T., Iliadis L.S., and Sylaios G.K. Time-series modeling of fishery landings using ARIMA models and fuzzy expected intervals software. Environmental Modelling & Software 21 12 (2006) 1711-1721
12. Lin Ch.-T., and Lee G. Neural - network-based fuzzy logic control and decision system. IEEE Transactions on Computers 40 12 (1991) 1320-1336
13. Lin Ch.-T., and Lee G. Reinforcement structure/parameter learning for neural-network-based fuzzy logic control systems. IEEE Transactions on Fuzzy Systems 2 1 (1994) 46-63
14. Lin Y., and Cunningham III G.A. A new approach to fuzzy-neural system modelling. IEEE Transactions on Fuzzy Systems 3 2 (1995) 190-198
15. Luo X., Hou W., Li Y., and Wang Z. A fuzzy neural network model for predicting clothing thermal comfort. Computers and Mathematics with Applications 53 12 (2007) 1840-1846
16. Modi P.K., Singh S.P., and Sharma J.D. Fuzzy neural network based voltage stability evaluation of power systems with SVC. Applied Soft Computing Journal 8 1 (2008) 657-665
17. Onkal-Engin G., Demir I., and Engin S.N. Determination of the relationship between sewage odour and BOD by neural networks. Environmental Modelling & Software 20 7 (2005) 843-850
18. Poggio T., and Girosi G. Networks for approximation and learning. Proceedings of the IEEE 78 9 (1990) 1481-1497
19. Raduly B., Gernaey K.V., Capodaglio A.G., Mikkelsen P.S., and Henze M. Artificial neural networks for rapid WWTP performance evaluation: methodology and case study. Environmental Modelling & Software 22 8 (2007) 1208-1216
20. Tamada T., Maruyma M., Nakamura Y., Abe S., and Maeda K. Water demand forecasting by memory based learning. Water Science and Technology 28 11-12 (1993) 133-140
21. Tanaka K., Sano M., and Watanab H. Modelling and control of carbon monoxide concentration using a neuro-fuzzy technique. IEEE Transactions on Fuzzy Systems 3 3 (1995) 271-279
22. Weigend A.S., Huberman B.A., and Rumenlhart D.E. Predicting sunspots and exchanges with connectionist network. Non-linear Modelling and Forecasting (1992), Addison-Wesley, Redwood City, CA EEUU
23. White, H., 1988. Economic prediction using neural networks: the case of IBM daily stock returns. In: Proceedings of the IEEE International Joint Conference on Neural Networks, San Diego, CA.
24. Wong F.S., Wang P.Z., Goh T.H., and Quek B.K. Fuzzy neural systems for stock selection. Financial Analyst Journal Jan-Feb (1992) 47-74
25. Yong M.A., Yongzhen P., and Shuying W. Intelligent control aeration and external carbon addition for improving nitrogen removal. Environmental Modeling & Software 21 6 (2006) 821-828