Prediction of Water Quality and Alum Dose using Artificial Neural Network-Case Study of Surat
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Amari, Murata, Muller, Finke, Yang, 1997. Asymptotic statistical theory of overtraining and cross-validation, IEEE Transactions on Neural Networks 8 (5), 985–996.
B. Raduly, 2008 “Artificial neural networks for rapid WWTP performance evaluation: Methodology and case study”, Seminar for Master of Science from university of Malasiya.
C.W. Baxter, S.J. Stanley, Q. Zhang, D.W. Smith; 2007; “Developing Artificial Neural Network Process Models: A Guide for Drinking Water Utilities”. 6th Environmental Engineering Specialty Conference of the CSCE & 2nd Spring Conference of the Geoenvironmentla, Division of the Canadian Geotechnical Society.
Chen, W.C., Chang, N.B., Shieh, W.K., 2001. Advanced hybrid fuzzy-neural controller for industrial wastewater treatment. Journal of Environmental Engineering 127 (11), 1048–1059.
Fernández, E. and Gálvis, A., “Artificial Neural Networks Model Used for Clear Water Treatment Plant”, seminar submitted to University of Colombia.
Gagnon, C., Grandjean, B.P.A., Thibault, J., 1997. Modelling of coagulant dosage in a water treatment plant. Artificial Intelligence in Engineering 11, 401–404.
Guan-De Wu, Shang-Lien Lo; 2008, “Predicting real-time coagulant dosage in water treatment by artificial neural networks and adaptive network-based fuzzy inference system”, Journal of Engineering Applications of Artificial Intelligence 21 (1189–1195).
Gulay TEZEL, Esra YEL and R.Kaan SINAN, 2010, “Artificial Neural Network (Ann) Model for Domestic Wastewater Treatment Plant Control”, BALWOIS - Ohrid, Republic of Macedonia – 25.
Holger R. Maier, Nicolas Morgan & Christopher W.K. Chow, 2003, “Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters”, Journal of Environmental Modeling & software Environmental Modelling & Software 19 (485–494).
Hornik, K., Stinchcombe, M., White, H., 1989. Multilayer feedforward networks are universal approximators. Neural Networks 2, 359–366.
Maged M. Hamed, Mona G. Khalafallah & Ezzat A. Hassanien; 2004; “Prediction of wastewater treatment plant performance using artificial neural networks”; Journal of Environmental Modeling & software 19 (919–928).
N. Valentin, 1994, “Modelling of coagulant dosage in a water treatment plant”, (56-57).
National Instruments, 1999. Labview 5.1, National Instruments, Austin, Texas, USA.
Serodes, J.B., Rodriguez, M.J., Ponton, A., 2001. Chlorcast: a methodology for developing decision-making tools for chlorine disinfection control. Environmental Modelling and Software 16, 53–62.
Weigend, A.S., Rumelhart, D.E., Huberman, B.A., 1990. Predicting the future: A connectionist approach. International Journal of Neural Systems 1 (3), 193–209.
White, H., 1989. Learning in artificial neural networks: A statistical perspective. Neural Computation 1, 425–464.
Zhang, Q., Stanley, S.J., 1999. Real-time water treatment process control with artificial neural networks. Journal of Environmental Engineering 125 (2), 153–160.
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