References

  1. P. Juntunen, M. Liukkonen, M. Pelo, M.J. Lehtola, Y. Hiltunen, Modelling of water quality: an application to a water treatment process, Appl. Comp. Intel. Soft Comp., 2012 (2012) 4.
  2. F. Edition, Guidelines for drinking-water quality, WHO Chron, 38 (2011) 104–108.
  3. R.D. Letterman, A.W.W. Association, Water Quality and Treatment, McGraw-Hill, 1999.
  4. M.L. Davis, Water and Wastewater Engineering, Design Principles and Practice, The Mc Graw-Hill Companies, Michigan State University,2010.
  5. F.-P.-T. C. o. D. Water, Turbidity in Drinking Water ed. Canada: Federal-Provincial-Territorial Committee 2012.
  6. G. Apostol, R. Kouachi, I. Constantinescu, Optimization of coagulation-flocculation process with aluminum sulfate based on response surface methodology, UPB Sci. Bull, Series B, 73 (2011) 77–84.
  7. L.S. Kang, J.L. Cleasby, Temperature effects on flocculation kinetics using Fe (III) coagulant, J. Environ. Eng., 121 (1995) 893.
  8. J.E. Van Benschoten, J.K. Edzwald, Chemical aspects of coagulation using aluminum salts—I. Hydrolytic reactions of alum and polyaluminum chloride, Water Res., 24 (1990) 1519–1526.
  9. J.E. Van Benschoten, J.K. Edzwald, Chemical aspects of coagulation using aluminum salts—II. Coagulation of fulvic acid using alum and polyaluminum chloride, Water Res., 24 (1990) 1527–1535.
  10. C. Huang, H. Shiu, Interactions between alum and organics in coagulation, Colloids Surf. A Physicochem. Eng. Asp., 113 (1996) 155–163.
  11. J. Xie, D. Wang, J. van Leeuwen, Y. Zhao, L. Xing, C.W. Chow, pH modeling for maximum dissolved organic matter removal by enhanced coagulation, J. Environ. Sci., 24 (2012) 276–283.
  12. S. Heddam, A. Bermad, N. Dechemi, ANFIS-based modelling for coagulant dosage in drinking water treatment plant: a case study, Environ. Monit. Assess., 184 (2012) 1953–1971.
  13. J. Bratby, Coagulation and Flocculation in Water and Wastewater Treatment, IWA Publishing, 2016.
  14. H.R. Maier, N. Morgan, C.W. Chow, Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters, Environ. Model Softw., 19 (2004) 485–494.
  15. Y. Zhao, Y. Wang, B. Gao, H. Shon, J.-H. Kim, Q. Yue, Coagulation performance evaluation of sodium alginate used as coagulant aid with aluminum sulfate, iron chloride and titanium tetrachloride, Desalination, 299 (2012) 79–88.
  16. S. Xia, X. Li, Q. Zhang, B. Xu, G. Li, Ultrafiltration of surface water with coagulation pretreatment by streaming current control, Desalination, 204 (2007) 351–358.
  17. J.C. Vickers, M.A. Thompson, U.G. Kelkar, The use of membrane filtration in conjunction with coagulation processes for improved NOM removal, Desalination, 102 (1995) 57–61.
  18. R. Bryant, Optimizing coagulation with the Streaming Current Monitor, J. New England Water Works Assoc., 110 (1996) 268–271.
  19. C. Baxter, S. Stanley, Q. Zhang, Development of a full-scale artificial neural network model for the removal of natural organic matter by enhanced coagulation, J. Water SRT., 48 (1999) 129–136.
  20. R.-F. Yu, S.-F. Kang, S.-L. Liaw, M.-C. Chen, Application of artificial neural network to control the coagulant dosing in water treatment plant, Water Sci. Technol., 42 (2000) 403–408.
  21. J.M. Montgomery, Water Treatment: Principles and Design, Published by John Wiley & Sons Ltd, New York, USA, 1985.
  22. D.-S. Joo, D.-J. Choi, H. Park, The effects of data preprocessing in the determination of coagulant dosing rate, Water Res., 34 (2000) 3295–3302.
  23. J.-L. Lin, C. Huang, J.R. Pan, D. Wang, Effect of Al (III) speciation on coagulation of highly turbid water, Chemosphere, 72 (2008) 189–196.
  24. J.H. Kweon, H.-W. Hur, G.-T. Seo, T.-R. Jang, J.-H. Park, K.Y. Choi, et al., Evaluation of coagulation and PAC adsorption pretreatments on membrane filtration for a surface water in Korea: A pilot study, Desalination, 249 (2009) 212–216.
  25. C. Gagnon, B. Grandjean, J. Thibault, Modelling of coagulant dosage in a water treatment plant, Artif. Intellig. Eng., 11 (1997) 401–404.
  26. G. Carrera, J. Aires-de-Sousa, Estimation of melting points of pyridinium bromide ionic liquids with decision trees and neural networks, Green Chem., 7 (2005) 20–27.
  27. G.-D. Wu, S.-L. Lo, Predicting real-time coagulant dosage in water treatment by artificial neural networks and adaptive network-based fuzzy inference system, Eng. Appl. Artif. Intell., 21 (2008) 1189–1195.
  28. A. Robenson, S. Shukor, N. Aziz, Development of process inverse neural network model to determine the required alum dosage at Segama Water Treatment Plant Sabah, Malaysia, Comp. Aided Chem. Eng., 27 (2009) 525–530.
  29. K.H. Reckhow, Water quality prediction and probability network models, Can. J. Fish Aquat. Sci., 56 (1999) 1150–1158.
  30. C. Baxter, Q. Zhang, S. Stanley, R. Shariff, R.-R. Tupas, H. Stark, Drinking water quality and treatment: the use of artificial neural networks, Can. J. Civ. Eng., 28 (2001) 26–35.
  31. W.A. Pike, Modeling drinking water quality violations with Bayesian networks, J. Am. Water Resour. Assoc., 40 (2004) 1563–1578.
  32. M. Najafzadeh, Neurofuzzy-based GMDH-PSO to predict maximum scour depth at equilibrium at culvert outlets, J. Pipeline Syst. Eng. Pract., 7 (2015) 06015001.
  33. M. Najafzadeh, Neuro-fuzzy GMDH systems based evolutionary algorithms to predict scour pile groups in clear water conditions, Ocean Eng., 99 (2015) 85–94.
  34. M. Najafzadeh, H. Bonakdari, Application of a neuro-fuzzy GMDH model for predicting the velocity at limit of deposition in storm sewers, J. Pipeline Syst. Eng. Pract., 8 (2016) 06016003.
  35. M. Najafzadeh, F. Saberi-Movahed, S. Sarkamaryan, NF-GMDH-based self-organized systems to predict bridge pier scour depth under debris flow effects, Marine Georesour. Geotechnol., (2017) 1–14.
  36. M. Najafzadeh, A. Tafarojnoruz, Evaluation of neuro-fuzzy GMDH-based particle swarm optimization to predict longitudinal dispersion coefficient in rivers, Environ. Earth Sci., 75 (2016) 157.
  37. M. Najafzadeh, G.-A. Barani, M.R.H. Kermani, Abutment scour in clear-water and live-bed conditions by GMDH network, Water Sci. Technol., 67 (2013) 1121–1128.
  38. M. Najafzadeh, G.-A. Barani, Comparison of group method of data handling based genetic programming and back propagation systems to predict scour depth around bridge piers, Scientia Iranica, 18 (2011) 1207–1213.
  39. M. Najafzadeh, H.M. Azamathulla, Group method of data handling to predict scour depth around bridge piers, Neural Comput. Applic., 23 (2013) 2107–2112.
  40. M. Najafzadeh, G.-A. Barani, M.-R. Hessami-Kermani, Group method of data handling to predict scour at downstream of a ski-jump bucket spillway, Earth Sci. Inform., 7 (2014) 231–248.
  41. A. Daghbandan, M. Akbarizadeh, M. Yaghoobi, Modeling and optimization of poly electrolyte dosage in water treatment process by GMDH type-NN and MOGA, Int. J. Chemoinform. Chem. Eng. (IJCCE), 3 (2013) 94–106.
  42. A.G. Ivakhnenko, Polynomial theory of complex systems, IEEE Trans. Syst. Man. Cybern., 1 (1971) 364–378.
  43. S. Farlow, Self-organizing method in modeling: GMDH type algorithm, ed: Marcel Dekker Inc., New York, 1984.
  44. A.G. Ivakhnenko, Polynomial theory of complex systems, Trans. Syst. Man. Cybern., 1 (1971) 364–378.
  45. N. Nariman-Zadeh, A. Darvizeh, M. Felezi, H. Gharababaei, Polynomial modelling of explosive compaction process of metallic powders using GMDH-type neural networks and singular value decomposition, Model Simul. Mat. Sci. Eng., 10 (2002) 727.
  46. A. Jamali, A. Hajiloo, N. Nariman-Zadeh, Reliability-based robust Pareto design of linear state feedback controllers using a multi-objective uniform-diversity genetic algorithm (MUGA), Expert Syst. Applic., 37 (2010) 401–413.
  47. A. Jamali, N. Nariman-zadeh, H. Ashraf, Z. Jamali, Robust Pareto Design of ANFIS Networks for Nonlinear Systems with Probabilistic Uncertainties, 2011, pp. 300–304.
  48. A. Jamali, N. Nariman-zadeh, K. Atashkari, Multi-objective uniform-diversity genetic algorithm (MUGA), Adv. Evolution. Algorithms, (2008) 978–983.
  49. N. Nariman-Zadeh, A. Darvizeh, M. Dadfarmai, Design of ANFIS networks using hybrid genetic and SVD methods for the modelling of explosive cutting process, J. Mater. Process. Technol., 155 (2004) 1415–1421.
  50. M. Najafzadeh, F. Saberi-Movahed, GMDH-GEP to predict free span expansion rates below pipelines under waves, Marine Georesour. Geotechnol., (2018) 1–18.