References

  1. S. Heddam, H. Lamda, S. Filali, Predicting effluent biochemical oxygen demand in a wastewater treatment plant using generalized regression neural network based approach: a comparative study, Environ. Process., 16 (2016), 153–165.
  2. I. Plazl, G. Pipus, M. Drolka, T. Koloini, Parametric sensitivity and evaluation of a dynamic model for single-stage wastewater treatment plant, Acta Chim. Slov., 46 (1999) 289–300.
  3. K.P. Singh, A. Basant, A. Malik, G. Jain, Artificial neural network modeling of the river water quality a case study, Ecol. Model., 220 (2009) 888–895.
  4. X. Wen, J. Fang, M. Diao, C. Zhang, Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China, Environ. Monit. Assess., 185 (2013), 4361–4371.
  5. A.N.S. Tomić, D.Z. Antanasijević, M.Đ. Ristić, A.A. Perić-Grujić, V.V. Pocajt, Modeling the bod of Danube River in Serbia using spatial, temporal, and input variables optimized artificial neural network models, Environ. Monit. Assess., 188 (2016).
  6. Q. Chen, A. Mynett, Modelling algal blooms in the Dutch coastal waters by integrated numerical and fuzzy cellular automata approaches, Ecol. Model., 199 (2006) 73–81.
  7. M.R. Kuppusamy, V.V. Giridhar, Factor analysis of water quality characteristics including trace metal speciation in the coastal environmental system of Chennai Ennore, Environ. Int., 32 (2006) 174–179.
  8. K.-W. Chau, N. Muttil, Data mining and multivariate statistical analysis for ecological system in coastal waters, J. Hydroinf., 9 (2007) 305–317.
  9. M.L. Wu, Y.S. Wang, Using Chemometeries to Evaluate Anthropogenic Effects in Daya Bay, China, Estuar, Coast. Shelf. Sci., 72 (2007) 732–742.
  10. A.F.M. Alkarkhi, A. Ahmad, A.M. Easa, Assessment of surface water quality of selected estuaries of Malaysia: multivariate statistical techniques, The Environmentalist, 29 (2009) 255–262.
  11. V. Kumar, A. Sharma, A. Chawla, R. Bhardwaj, K.T. Ashwani, Water quality assessment of river Beas, India, using multivariate and remote sensing techniques, Environ. Monit. Assess., 188 (2016) 137.
  12. B.K. McCabe, I. Hamawand, C. Baillie, Investigating wastewater modelling as a tool to predict anaerobic decomposition and biogas yield of abattoir effluent, J. Environ. Chem. Eng., 1 (2013) 1375- 1379.
  13. M.W. Lee, S.H. Hong, H. Choi, J.-H. Kim, D.S. Lee, J.M. Park, Real–time remote monitoring of small-scaled biological wastewater treatment plants by a multivariate statistical process control and neural network-based software sensors, Process Biochem., 43 (2008) 1107–1113.
  14. J. Tomperi, E. Koivuranta, A. Kuokkanen, K. Leiviskä, Modelling effluent quality based on a real-time optical monitoring of the wastewater treatment process, Environ. Technol., 38 (2017) 1-13,
  15. K.P. Oliveira-Esquerre, M. Mori, R.E. Bruns, Simulation of an industrial wastewater treatment plant using artificial neural networks and principal components analysis, Braz. J. Chem. Eng., 19 (2002), 365-370.
  16. S. Acikalin, R. Ileri, R. Keles, Estimation of Outflow Water Parameters and Yield Values of Adapazari Urban Wastewater Treatment Plant by Artificial Neural Networks, Üniversite Öğrencileri 2. Çevre Sorulari Kongresi, Istanbul, (In Turkish), (2007) 100–107.
  17. D. Guclu, Modeling of Full Scale Urban Wastewater Treatment Plants by Using Computer Program and Investigation of Treatment Performances, Phd Thesis, Selcuk University, Institute of Science and Technology, Konya (In Turkish), 2007.
  18. E. Dogan, R. Koklu, B. Sengorur, Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique, J. Environ. Manage., 90 (2009) 1229–1235.
  19. O.E. Denizci, Dynamic Simulation of Activated Sludge Systems: Investigation of Tuzla and Pasaköy Domestic Wastewater Treatment Plants in Istanbul, Master’s Thesis, Yildiz Technic University, Institute of Science and Technology, İstanbul (In Turkish) 2009.
  20. Y-S.T. Hong, M.R. Rosen, R. Bhamidimarri, Analysis of a municipal wastewater treatment plant using a neural networkbased pattern analysis, Water Res., 37 (2003) 1608–1618.
  21. O.T. Baki, E. Aras, Estimation of BOD in wastewater treatment plant by using different ANN algorithms, Membr. Water Treat., 9 (2018) 455-462.
  22. Y. Ma, M. Huang, J. Wan, K. Hu, Y. Wang, H. Zhang, Hybrid artificial neural network genetic algorithm technique for modeling chemical oxygen demand removal in anoxic/oxic process, J. Environ. Sci. Health A Tox. Hazard Subst. Environ. Eng., 46(2011) 574–580.
  23. H. Guo, K. Jeong, J. Lim, J. Jo, Y.M. Kim, J-P. Park, J.H. Kim, K.H. Cho, Prediction of effluent concentration in a wastewater treatment plant using machine learning models, J. Environ. Sci., 32 (2015) 90–101.
  24. Y.C. Huang, X.Z. Wang, Application of fuzzy causal networks to waste water treatment plants, Chem. Eng. Sci., 54 (1999) 2731-2738.
  25. G. Civelekoglu, Modeling of Treatment Processes with Artificial Intelligence and Multiple Statistical Methods, Ph.D Thesis, Suleyman Demirel University, Institute of Science and Technology, Isparta (In Turkish), 2006.
  26. G. Civelekoglu, N.O. Yigit, E. Diamadopoulos, M. Kitis, Modelling of COD removal in a biological wastewater treatment plant using adaptive neuro-fuzzy inference system and artificial neural network, Water Sci. Technol., 60 (2009) 1475–1487.
  27. T.-Y. Pai, S.C. Wang, C.F. Chiang, H.C. Su, L.F. Yu, P.J. Sung, C.Y. Lin, H.C. Hu,. Improving Neural Network Prediction of Effluent from Biological Wastewater Treatment Plant of Industrial Park Using Fuzzy Learning Approach, Bioprocess Biosyst. Eng., 32 (2009) 781–790.
  28. M.M. Hamed, M.G. Khalafallah, E.A. Hassanien, Prediction of wastewater treatment plant performance using artificial neural networks, Environ. Model. Softw., 19 (2004) 919–928.
  29. G. Onkal-Engin, I. Demir, S.N. Engin, Determination of the relationship between sewage odour and BOD by neural network, Environ. Model. Softw., 20 (2005) 843–850.
  30. F.S. Mjalli, S. Al-Asheh, H.E. Alfadala, Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance, J. Environ. Manage., 83 (2007) 329–338.
  31. E.R. Rene, M.B. Saidutta, Prediction of Water Quality Indices by Regression Analysis and Artificial Neural Networks, Int. J. Environ. Res., 2 (2008) 183–188.
  32. 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. Banner, 27 (2008) 439–446.
  33. J.-W. Lee, C. Suh, Y.-S.T. Hong, H.-S. Shin, Sequential modelling of a full-scale wastewater treatment plant using an artificial neural network, Bioprocess Biosyst. Eng., 34 (2011) 963–973.
  34. A.K. Verma, T.N. Singh, Prediction of water quality from simple field parameters, Environ. Earth Sci., 69 (2013) 821–829.
  35. H.Z. Abyaneh, Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters, J. Environ. Health Sci. Eng., 12 (2014) 40.
  36. X. Li, J. Song, A New ANN-Markov Chain Methodology for Water Quality Prediction, 2015 Int. Joint Conf. on Neural Networks (IJCNN), Killarney, Ireland, 2015.
  37. A. Vijayan, G.S. Mohan, Prediction of effluent treatment plant performance in a diary industry using artificial neural network technique, J. Civil Environ. Eng., (2016) 6.
  38. M. Ebrahimi, E.L. Gerber, T.D. Rockaway, Temporal performance assessment of wastewater treatment plants by using multivariate statistical analysis. J. Environ. Manage., 193 (2017) 234–246.
  39. O.T. Baki, Modeling of Biochemical Oxygen Demand on Wastewater Treatment Plant by using Different Artificial Intelligence Methods: Antalya Hurma Wastewater Treatment Plant Example, Master Thesis, Karadeniz Technical University, Institute of Science and Technology, Trabzon, 2016 (In Turkish).
  40. O. Kisi, K.S. Parmar, Application of Least Square Support Vector Machine and Multivariate Adaptive Regression Spline Models in Long Term Prediction of River Water Pollution, J. Hydrol., 534 (2016) 104-112.
  41. R.C. Deo, O. Kisi, V.P. Singh, Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model, Atmos. Res., 184 (2017) 149–175.
  42. D. Karaboga, An Idea on Honey Bee Swarm for Numerical Optimization, Technical Report-TR06, 2005.
  43. C. Ozkan, O. Kisi, B. Akay, Neural networks with artificial bee colony algorithm for modeling daily reference evapotranspiration, Irrig. Sci., 29 (2011) 431–441.
  44. R.V. Rao, V. Patel, An elitisit teaching-learning-based optimization algorithm for solving complex constrained optimization problems, Int. J. Ind. Eng. Comput., 3 (2012) 535–560.
  45. S.C. Satapathy, A. Naik, Data Clustering Based on Teaching Learning Based Optimization, SEMCCO 2011, Part II, LNCS 7077 (2011) 148–156.
  46. V. Togan, Design of Planar Steel Frames Using Teaching-Learning Based Optimization, Eng. Struct., 35 (2012) 225–232.
  47. E. Uzlu, M.I. Komurcu, M. Kankal, T. Dede, H.T. Ozturk, Prediction of berm geometry using a set of laboratory tests combined with teaching-learning-based optimization and artificial bee colony algorithms, Appl. Ocean Res., 48 (2014) 103–113.
  48. A. Bayram, E. Uzlu, M. Kankal, T. Dede, Modeling stream dissolved oxygen concentration using teaching–learning based optimization algorithm, Environ. Earth Sci., 73 (2015) 6565–6576
  49. V.N. Sharda, R.M. Patel, S.O. Prasher, P.R. Ojasvi, C. Prakash. Modeling runoff from middle Himalayan watersheds employing artificial intelligence techniques, Agric. Water Manage., 83 (2006) 233–242.
  50. A.H. Bhatt, R.V. Karanjekar, S. Altouqi, M.L. Sattler, M.D.S. Hossain, V.P. Chen, Estimating landfill leachate BOD and COD based on rainfall, ambient temperature, and waste composition: exploration of a MARS statistical approach, Environ. Technol. Innovation, 8 (2017) 1–16.