References

  1. M.C. Jami, I.A. Husain, N.A. Abdullah, Multiple inputs artificial neural network model for the prediction of wastewater treatment plant performance, J. AJBAS., 6(1) (2012) 62–69.
  2. D. Hanby, I. Tukoglu, Y. Demir, Prediction of wastewater treatment plant performance based on wavelet packet decomposition and neural networks, Expert Syst. Appl., 34(2) (2008) 1038–1043.
  3. C.H. Wen, C.A. Vassiliadis, Applying hybrid artificial intelligence techniques in wastewater treatment, Eng. Appl. ArtifIntell., 11(6) (1998) 685–705.
  4. I. Zaheer, C.G. Bai, Application of artificial neural network for water quality management, LTI., 5(2) (2003) 10–15.
  5. A. Nabavi-Pelesaraei, R. Bayat, H. Hosseinzadeh-Bandbafha, H. Afrasyabi, K.W. Chau, Modeling of energy consumption and environmental life cycle assessment for incineration and landfill systems of municipal solid waste management-A case study in Tehran Metropolis of Iran, J. Clean Prod., 148 (2107) 427–440.
  6. Y. Seo, S. Kim, V.P. Singh, Comparison of different heuristic and decomposition techniques for river stage modeling, Environ. Monit. Assess., 190(7) (2018) 392.
  7. E. Olyaie, H. Banejad, K.W. Chau, A.M. Melesse, A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: A case study in United States, Environ. Monit. Assess., 187(4) (2015) 189.
  8. S. Shamshirband, E. Jafari Nodoushan, J.E. Adolf, A. Abdul Manaf, A. Mosavi, K.W. Chau, Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters, Eng. Appl. Comp. Fluid., 13(1) (2019) 91–101.
  9. M. Djeddou, B. Achour, The use of a neural network technique for the prediction of sludge volume index in municipal wastewater treatment plant, LARHYSS J., 24 (2015) 351–370.
  10. I. Lou, Y. Zhao, Sludge bulking prediction using principle component regression and artificial neural network, Math. Probl. Eng., (2012) Article ID 237693.
  11. E. Dogan, A. Ates, E.C. Yilmaz, B. Eren, Application of artificial neural networks to estimate wastewater treatment plant inlet biochemical oxygen demand, Environ. Prog. Sustain., 27(4) (2008) 439–446.
  12. A.E. Tumer, S. Edebali, An artificial neural network model for wastewater treatment plant of Konya, IJISAE., 3(4) (2015) 131–13.
  13. M. Vyas, B. Modhera, V. Vyas, A.k. Sharma, Performance forecasting of common effluent treatment plant parameters by artificial neural network, JEAS., 6(1) (2011) 38–42.
  14. D.J. Choi, H. Park, A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process, Water Res., 35(16) (2001) 3959–3967.
  15. M.M. Hamed, M.G. Khalafallah, E.A. Hassanien, Prediction of wastewater treatment plant performance using artificial neural networks, Environ. Model Softw., 19(10) (2004) 919–928.
  16. S.I. Abba, G. Elkiran, Effluent prediction of chemical oxygen demand from the WWTP using artificial neural network application, Procedia. Comput. Sci., 120 (2017) 156–163.
  17. H.G. Han, J.F. Qiao, Q.L. Chen, Model predictive control of dissolved oxygen concentration based on a self-organizing RBF neural network, Control. Eng. Pract., 20(4) (2012) 465–476.
  18. Y.S.T. Hong, M.R. Rosen, R. Bhamidimarri, Analysis of a municipal wastewater treatment plant using a neural network-based pattern analysis, Water Res., 37(7) (2003) 1608–1618.
  19. G.J. Bowden, G.C. Dandy, H.R. Maier, Input determination for neural network models in water resources applications. Part 1—background and methodology, J. Hydrol., 301(4) (2005) 75–92.
  20. S.E. Kim, I.S. Won, Artificial Neural Network ensemble modeling with conjunctive data clustering for water quality prediction in rivers, J. Hydro-Environ. Res., 9(3) (2015) 325–339.
  21. J. Wan, Prediction of effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy inference system, Appl. Soft. Comput., 11(3) (2011) 3238–3246.
  22. V. Nourani, M. Mehrvand, A.H. Baghanam, Implication of SOM-ANN based clustering for multistation rainfall-runoff modeling, JUEE., 8(2) (2014) 198–210.
  23. T.Y. Pai, P.Y. Yang, S.C. Wang, M.H. Lo, C.F. Chiang, J.L. Kuo, Y.H. Chang, Predicting effluent from the wastewater treatment plant of industrial park based on fuzzy network and influent quality, Appl. Math. Model., 35(8) (2011) 3674–3684.
  24. O. Çinar, New tool for evaluation of performance of wastewater treatment plant: artificial neural network, Process Biochem., 40(9) (2005) 2980–2984.
  25. T. Kohonen, The self-organizing map, Neurocomputing., 21(1–3) (1998) 1–6.
  26. K.K. Jassar, K.S. Dhindsa, Comparative study and performance analysis of clustering algorithms, IJCA, 975 (2015) 8887.
  27. D. Aguado, T. Montoya, L. Borras, A. Seco, J. Ferrer, Using SOM and PCA for analyzing and interpreting data from a P-removal SBR, Eng. Appl. ArtifIntell., 21(6) (2008) 919–930.
  28. R. Sathya, A. Abraham, Comparison of supervised and unsupervised learning algorithms for pattern classification, IJARAI., 2(2) (2013) 34–38.
  29. V. Nourani, G. Andalib, D. Dąbrowska, Conjunction of wavelet transform and SOM-mutual information data pre-processing approach for AI-based Multi-Station nitrate modeling of watersheds, J. Hydrol., 548 (2017) 170–183.
  30. J. Qiao, H. Zhiqiang, L. Wenjing, Soft measurement modeling based on chaos theory for biochemical oxygen demand (BOD), Water., 8(12) (2016) 581.
  31. D.A. Cancilla, X. Fang, Evaluation and quality control of environmental analytical data from the Niagara River using multiple chemometric methods, Great Lakes Res., 22(2) (1996) 241–253.
  32. T.R. Holcomb, M. Morari, PLS/neural networks, Comput. Chem. Eng., 16(4) (1992) 393–411.
  33. A. Singh, M. Imtiyaz, R.K. Isaac, D.M. Denis, Comparison of soil and water assessment tool (SWAT) and multilayer perceptron (MLP) artificial neural network for predicting sediment yield in the Nagwa agricultural watershed in Jharkhand, India, Agr. Water Manage., 104 (2012) 113–120.
  34. K. Hornik, S. Maxwell, W. Halbert, Multilayer feed forward networks are universal approximators, Neural Netw., 2(5) (1989) 359–366.
  35. T.W. Kim, J.B. Valdés, Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks, J. Hydrol. Eng., 8(6) (2003) 319–328.
  36. A.R. Pendashteh, A. Fakhru’l-Razi, N. Chaibakhsh, L.C. Abdullah, S.S. Madaeni, Z.Z. Abidin, Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network, J. Hazard. Mater., 192(2) (2011) 568–575.
  37. D.R. Legates, J.G. McCabe, Evaluating the use of “goodness-offit” measures in hydrologic and hydroclimatic model validation, Water Resour. Res., 35(1) (1999) 233–241.
  38. G.B. Sahoo, C. Ray, Predicting flux decline in crossflow membranes using artificial neural networks and genetic algorithms, J Membr. Sci., 283 (2006) 147–157.
  39. C. Rosén, J. Röttorp, U. Jeppsson, Multivariate on-line monitoring: challenges and solutions for modern wastewater treatment operation, Water Sci. Technol., 47(2) (2003) 171–179.
  40. P. Teppola, S.P. Mujunen, P. Minkkinen, A combined approach of partial least squares and fuzzy c-means clustering for the monitoring of an activated- sludge waste-water treatment plant, ChemometrIntell. Lab. Syst., 41(1) (1998) 95–103.
  41. H. Çamdevýren, N. Demýr, A. Kanik, S. Keskýn, Use of principal component scores in multiple linear regression models for prediction of Chlorophyll-a in reservoirs, Ecol. Modell., 181(4) (2005) 581–589.
  42. J.C. Davis, Statistical and Data Analysis in Geology, 2nd ed, John Wiley and Sons. New York, 1986.
  43. H. Wackernagel, Multivariate Geostatistics: An Introduction With Applications, New York and London, 1995.
  44. R. Steuer, J. Kurths, C.O. Daub, J. Weise, J. Selbig, The mutual information detecting and evaluating dependencies between variables, Bioinformatics, 18(2) (2002) 231–240.
  45. C.E. Shannon, A note on the concept of entropy, Bell System Tech J., 27(3) (1948) 379–423.
  46. M.S. Babel, G.B. Badgujar, V.R. Shinde, Using the mutual information technique to select explanatory variables in artificial neural networks for rainfall forecasting, Meteorol. Appl., 22(3) (2015) 610–616.
  47. T.M. Cover, J.A. Thomas, Elements of information theory, John Wiley & Sons, 2012.
  48. R.K. Parviz, N. Mozayani, M.J. Motlagh, Mutual information-based input variable selection algorithm and wavelet neural network for time series prediction, International Conference on Artificial Neural Networks. Springer, Berlin, Heidelberg, 2008.
  49. G. Brown, A. Pocock, M.J. Zhao, M. Luján, Conditional likelihood maximisation: a unifying framework for information theoretic feature selection, J. Mach. Learn. Res., (2012) 27–66.
  50. T. Kohonen, Self-Organizing Maps, Springer, 1997.
  51. H.L. Garcıá , I.M. González, Self-organizing map and clustering for wastewater treatment monitoring, Eng. Appl. ArtifIntell., 17(3) (2004) 215–225.
  52. T. Kohonen, S. Kaski, H. Lappalainen, Self-organized formation of various invariant-feature filters in the adaptive-subspace SOM, Neural. Comput., 9(6) (1997) 1321–1344.
  53. M. Hamada, A.Z. Hossam, A.J. Ahmed, Application of artificial neural networks for the prediction of Gaza wastewater treatment plant performance-Gaza strip, JARWW., 5(1) (2018) 399–406.
  54. A. Sharma, C.W. Omlin, Performance comparison of particle swarm optimization with traditional clustering algorithms used in self organizing map, Int. J. Comput. Int. Sys., 5(1) (2019) 1–12.