1. Maest AS, Kuipers JR, Travers CL, Atkins DA. Predicting water quality at hardrock mines: methods and models, uncertainties, and state-of-the-art. Washington, DC: Earthworks; 2005.
2. Witten IH, Frank E, Hall MA. Data mining: practical machine learning tools and techniques. 2nd ed. Burlington, MA: Morgan Kaufmann; 2005.
3. Zhang G, Eddy Patuwo B, Hu MY. Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting 2013; 14(1): 35-62.
4. Chen Y, Yang B, Dong J, Abraham A. Time-series forecasting using flexible neural tree model. Information Sciences 2013; 174(3-4): 219-35.
5. Giordano F, La Rocca M, Perna C. Forecasting nonlinear time series with neural network sieve bootstrap. Computational Statistics & Data Analysis 2007; 51(8): 3871-84.
6. Lapedes A. Nonlinear signal processing using neural networks: Prediction and system modelling. Los Alamos National Laboratory; 1987.
7. Imrie CE, Durucan S, Korre A. River flow prediction using artificial neural networks: generalisation beyond the calibration range. Journal of Hydrology 2000; 233(1-4): 138-53.
8. Wu GD, Lo ShL. Effects of data normalization and inherent-factor on decision of optimal coagulant dosage in water treatment by artificial neural network. Expert Systems with Applications 2010; 37(7): 4974-83.
9. Melessea AM, Ahmad S, McClaina ME, Wangc X, Limd YH. Suspended sediment load prediction of river systems: An artificial neural network approach. Agricultural Water Management 2011; 98(5): 855-66.
10. Huo Sh, He Z, Su J, Xi B, Zhu Ch. Using artificial neural network models for eutrophication prediction. Procedia Environmental Sciences 2013; 18: 310-6.
11. Patil K, Deo MC, Ghosh S, Ravichandran M. Predicting sea surface temperatures in the North Indian Ocean with nonlinear autoregressive neural networks. International Journal of Oceanography 2013; 2013.
12. Delgrangea N, Cabassuda C, Cabassudb N, Durand-Bourlierc L, Lainec JM. Neural networks for prediction of ultrafiltration transmembrane pressure – application to drinking water production. Journal of Membrane Science 1998; 150(1): 111-23.
13. Looney CG. Pattern recognition using neural networks: theory and algorithms for engineers and scientists. New York, NY: Oxford University Press Inc; 1997.
14. Demuth H, Beale M, Hagan M. Neural network toolbox™ 6. user guide. Natick, MA: Math Works; 2008.
15. Hagan MT, Demuth HB, Beale MH. Neural network design. Stamford, CT: Thomson Learning; 1996.
16. Teng CM. Correcting noisy data. Proceedings of 16th International Conference on Machine Learning; 1999 Jun 27-30; San Francisco, USA.
17. Arthur RM. Application of on-line analytical instrumentation to process control. Proceedings of the 1st Annual Conference on Activated Sludge Process Control; 1982;. Chicago, Ill, USA.
18. Tarassenko LA. Guide to neural computing applications. Oxford, UK: Butterworth-Heinemann; 1998.
19. Olssen G, Newell B. Wastewater treatment systems, modelling, diagnosis and control. London, UK: IWA Publishing; 1999.
20. Masters T. Practical neural network recipes in C++. San Diego, CA: Academic Press; 1993.
21. Hyndman RJ, Koehler AB. Another look at measures of forecast accuracy. International Journal of Forecasting 2006; 22(4): 679-88.
22. Rumelhart DE, Hinton GE, Williams RJ. Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL, PDP Research Group. Parallel distributed processing: explorations in the microstructure of cognition. Cambridge, MA: A Bradford Book; 1987.
23. Zhu J, Zurcher J, Rao M, Meng MQH. An on-line wastewater quality predication system based on a time-delay neural network. Engineering Applications of Artificial Intelligence, 1998; 11(6): 747-58.
24. Pai TY, Tsai YP, Lo HM, Tsai CH, Lin.CY. Grey and neural network prediction of suspended solids and chemical oxygen demand in hospital wastewater treatment plant effluent. Computers & Chemical Engineering, 2007; 31(10): 1272-81.
25. El-Din AG, Smith DW. A neural network model to predict the wastewater inflow incorporating rainfall events. Water Res 2002; 36(5): 1115-26.
26. Zhang, Q, Stanley SJ. Real-time water treatment process control with artificial neural networks. Journal of Environmental Engineering 1999; 125(2): 153-60.
27. Zhang, Q, Stanley SJ. Forecasting raw-water quality parameters for the North Saskatchewan River by neural network modeling. Water Res 1997; 31(9): 2340-50.
28. Lamrini B, Benhammou A, Le Lann MV, Karama A. A neural software sensor for online prediction of coagulant dosage in a drinking water treatment plant. Transactions of the Institute of Measurement and Control 2005; 27(3): 195-213.