Applications of Soft Computing Methods in Environmental Engineering

Living reference work entry

Abstract

Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In environmental engineering, researchers and engineers have successfully employed different methods of soft computing for modeling of various real-life environmental problems. In this study, applications of core soft computing techniques, such as artificial neural networks (ANN), fuzzy logic (FL), adaptive neuro fuzzy inference systems (ANFIS), and support vector machines (SVM), are investigated and important mathematical aspects of these methods are highlighted. Considering the concepts and methods, this study briefly reviews applications of soft computing techniques in the field of environmental engineering, especially in water/wastewater treatment and air quality/pollution control/forecasting. A brief introduction to complexity of environmental problems and the general definition soft computing concept are presented in the first section of this chapter. The second section comprises four subsections and presents mathematical background of four different soft computing methods. Section “Implementation of Soft Computing Methods in Environmental Engineering”, which is consisted of eight subsections, reviews successful applications of soft computing-based prediction models implemented in the field of environmental engineering and summarizes the important findings obtained in these studies. At the end of the overview of the published works on soft computing applications in different environmental areas, in the last section, some special illustrative soft computing examples and the respective MATLAB®-based solutions are presented for environmental engineers.

Keywords

Adaptive neuro fuzzy inference systems (ANFIS) Aggregation Air quality/pollution control/forecasting Algorithm Artificial neural networks (ANN) Backpropagation Bayesian regulation Broyden–Fletcher–Goldfarb–Shanno (BFGS) Centroid Classification Defuzzification Early stopping Environmental engineering Feed-forward Firing strength Fletcher–Reeves Fuzzification Fuzzy inference system (FIS) Fuzzy logic (FL) Fuzzy operator Gradient descent Hessian matrix Implication Jacobian matrix Kernel functions Lagrange multipliers Levenberg–Marquardt Linguistic Logarithmic sigmoid MATLAB® Membership function Modeling Momentum factor Normalized layer (N) Polak–Ribiére Powell-Beale Prediction Product layer (π) Quasi–Newton Scaled conjugate gradient Soft computing Support vector machines (SVM) Tangent sigmoid Training Water and wastewater treatment 

References

  1. Abdul-Wahab SA, Al-Alawi SM (2002) Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks. Environ Model Softw 17(3):219–228CrossRefGoogle Scholar
  2. Acaroglu O, Ozdemir L, Asbury B (2008) A fuzzy logic model to predict specific energy requirement for TBM performance prediction. Tunn Undergr Space Technol 23(5):600–608CrossRefGoogle Scholar
  3. Adriaenssens V, Goethals PL, De Pauw N (2006) Fuzzy knowledge-based models for prediction of Asellus and Gammarus in watercourses in Flanders (Belgium). Ecol Model 195(1):3–10CrossRefGoogle Scholar
  4. Agirre-Basurko E, Ibarra-Berastegi G, Madariaga I (2006) Regression and multilayer perceptron-based models to forecast hourly O3 and NO2 levels in the Bilbao area. Environ Model Softw 21(4):430–446CrossRefGoogle Scholar
  5. Akkoyunlu A, Yetilmezsoy K, Erturk F, Oztemel E (2010) A neural network-based approach for the prediction of urban SO2 concentrations in the Istanbul metropolitan area. Int J Environ Pollut 40(4):301–321CrossRefGoogle Scholar
  6. Akkurt S, Tayfur G, Can S (2004) Fuzzy logic model for the prediction of cement compressive strength. Cem Concr Res 34(8):1429–1433CrossRefGoogle Scholar
  7. Al-Alawi SM, Abdul-Wahab SA, Bakheit CS (2008) Combining principal component regression and artificial neural networks for more accurate predictions of ground-level ozone. Environ Model Softw 23(4):396–403CrossRefGoogle Scholar
  8. Al-Daoud E (2009) A comparison between three neural network models for classification problems. J Artif Intell 2(2):56–64CrossRefGoogle Scholar
  9. Altunkaynak A, Özger M, Çakmakci M (2005) Water consumption prediction of Istanbul city by using fuzzy logic approach. Water Resour Manag 19(5):641–654CrossRefGoogle Scholar
  10. Antwi P, Li J, Boadi PO, Meng J, Shi E, Deng K, Bondinuba FK (2017) Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with backpropagation artificial neural network. Bioresour Technol 228:106–115CrossRefGoogle Scholar
  11. Assimakopoulos MN, Dounis A, Spanou A, Santamouris M (2013) Indoor air quality in a metropolitan area metro using fuzzy logic assessment system. Sci Total Environ 449:461–469CrossRefGoogle Scholar
  12. Atmaca H, Cetisli B, Yavuz HS (2001) The comparison of fuzzy inference systems and neural network approaches with ANFIS method for fuel consumption data. In: Second international conference on electrical and electronics engineering papers ELECO’2001, Bursa, TurkeyGoogle Scholar
  13. Ausati S, Amanollahi J (2016) Assessing the accuracy of ANFIS, EEMD-GRNN, PCR, and MLR models in predicting PM2.5. Atmos Environ 142:465–474CrossRefGoogle Scholar
  14. Bai Y, Li Y, Wang X, Xie J, Li C (2016) Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions. Atmos Pollut Res 7(3):557–566CrossRefGoogle Scholar
  15. Bashiri S, Akbarzadeh A, Zarrabi M, Yetilmezsoy K, Fingas M, Moosakhaani M (2017) Using PCA combined SVM in the classification of eutrophication in Dez Reservoir (Iran). Environ Eng Manag J. in pressGoogle Scholar
  16. Biancofiore F, Busilacchio M, Verdecchia M, Tomassetti B, Aruffo E, Bianco S, Tommaso SD, Colangeli C, Rosatelli G, Di Carlo P (2017) Recursive neural network model for analysis and forecast of PM10 and PM2.5. Atmos Pollut Res 8:652–659CrossRefGoogle Scholar
  17. Bıyıkoglu A, Akcayol MA, Özdemir V, Sivrioglu M (2005) Temperature prediction in a coal fired boiler with a fixed bed by fuzzy logic based on numerical solution. Energy Convers Manag 46(1):151–166CrossRefGoogle Scholar
  18. Cai M, Yin Y, Xie M (2009) Prediction of hourly air pollutant concentrations near urban arterials using artificial neural network approach. Transp Res Part D: Transp Environ 14(1):32–41CrossRefGoogle Scholar
  19. Cakmakci M (2007) Adaptive neuro-fuzzy modelling of anaerobic digestion of primary sedimentation sludge. Bioprocess Biosyst Eng 30(5):349–357CrossRefGoogle Scholar
  20. Cakmakci M, Kınacı C (2008) Adaptive neuro-fuzzy modeling of head loss in iron removal with rapid sand filtration. Water Environ Res 80(12):2268–2275CrossRefGoogle Scholar
  21. Cakmakci M, Kinaci C, Bayramoğlu M, Yildirim Y (2010) A modeling approach for iron concentration in sand filtration effluent using adaptive neuro-fuzzy model. Expert Syst Appl 37(2):1369–1373CrossRefGoogle Scholar
  22. Carbajal-Hernández JJ, Sánchez-Fernández LP, Carrasco-Ochoa JA, Martínez-Trinidad JF (2012) Assessment and prediction of air quality using fuzzy logic and autoregressive models. Atmos Environ 60:37–50CrossRefGoogle Scholar
  23. Chaloulakou A, Grivas G, Spyrellis N (2003) Neural network and multiple regression models for PM10 prediction in Athens: a comparative assessment. J Air Waste Manage Assoc 53(10):1183–1190CrossRefGoogle Scholar
  24. Chelani AB (2005) Predicting chaotic time series of PM10 concentration using artificial neural network. Int J Environ Stud 62(2):181–191CrossRefGoogle Scholar
  25. Civelekoglu G, Perendeci A, Yigit NO, Kitis M (2007) Modeling carbon and nitrogen removal in an industrial wastewater treatment plant using an adaptive network-based fuzzy inference system. Clean–Soil, Air, Water 35(6):617–625CrossRefGoogle Scholar
  26. Daneshvar N, Khataee AR, Djafarzadeh N (2006) The use of artificial neural networks (ANN) for modeling of decolorization of textile dye solution containing CI Basic Yellow 28 by electrocoagulation process. J Hazard Mater 137(3):1788–1795CrossRefGoogle Scholar
  27. Dwarakish GS, Nithyapriya B (2016) Application of soft computing techniques in coastal study – a review. J Ocean Eng Sci 1(4):247–255CrossRefGoogle Scholar
  28. Elangasinghe MA, Singhal N, Dirks KN, Salmond JA (2014) Development of an ANN–based air pollution forecasting system with explicit knowledge through sensitivity analysis. Atmos Pollut Res 5(4):696–708CrossRefGoogle Scholar
  29. Erdirencelebi D, Yalpir S (2011) Adaptive network fuzzy inference system modeling for the input selection and prediction of anaerobic digestion effluent quality. Appl Math Model 35(8):3821–3832CrossRefGoogle Scholar
  30. Esmaeelzadeh R, Dariane AB (2014) Long-term streamflow forecasting by adaptive Neuro–Fuzzy Inference System using K-fold cross-validation: (case study: Taleghan Basin, Iran). J Water Sci Res 6(1):71–83Google Scholar
  31. Feng X, Li Q, Zhu Y, Hou J, Jin L, Wang J (2015) Artificial neural networks forecasting of PM 2.5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmos Environ 107:118–128CrossRefGoogle Scholar
  32. Firat M, Gungor M (2007) River flow estimation using adaptive neuro fuzzy inference system. Math Comput Simul 75(3):87–96CrossRefGoogle Scholar
  33. Firat M, Turan ME, Yurdusev MA (2009) Comparative analysis of fuzzy inference systems for water consumption time series prediction. J Hydrol 374(3):235–241CrossRefGoogle Scholar
  34. Garcia Nieto PG, Alonso Fernández J, de Cos Juez FJ, Sánchez Lasheras F, Diaz Muñiz C (2013) Hybrid modelling based on support vector regression with genetic algorithms in forecasting the cyanotoxins presence in the Trasona reservoir (Northern Spain). Environ Res 122:1–10CrossRefGoogle Scholar
  35. Ghaedi AM, Vafaei A (2017) Applications of artificial neural networks for adsorption removal of dyes from aqueous solution: a review. Adv Colloid Interf Sci 245:20–39CrossRefGoogle Scholar
  36. Gocić M, Motamedi S, Shamshirband S, Petković D, Ch S, Hashim R, Arif M (2015) Soft computing approaches for forecasting reference evapotranspiration. Comput Electron Agric 113:164–173CrossRefGoogle Scholar
  37. Goodarzi M, Olivieri AC, Freitas MP (2009) Principal component analysis-adaptive neuro-fuzzy inference systems (ANFISs) for the simultaneous spectrophotometric determination of three metals in water samples. Spectrochim Acta A Mol Biomol Spectrosc 73(4):608–614CrossRefGoogle Scholar
  38. Grivas G, Chaloulakou A (2006) Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece. Atmos Environ 40(7):1216–1229CrossRefGoogle Scholar
  39. Guo H, Jeong K, Lim J, Jo J, Kim YM, Park J-P, Kim JH, Cho KH (2015) Prediction of effluent concentration in a wastewater treatment plant using machine learning models. J Environ Sci 32:90–101CrossRefGoogle Scholar
  40. Hamed MM, Khalafallah MG, Hassanien EA (2004) Prediction of wastewater treatment plant performance using artificial neural networks. Environ Model Softw 19(10):919–928CrossRefGoogle Scholar
  41. Hlihor RM, Diaconu M, Leon F, Curteanu S, Tavares T, Gavrilescu M (2015) Experimental analysis and mathematical prediction of Cd (II) removal by biosorption using support vector machines and genetic algorithms. New Biotechnol 32(3):358–368CrossRefGoogle Scholar
  42. Hu YF, Yang CZ, Dan JF, Pu WH, Yang JK (2017) Modeling of expanded granular sludge bed reactor using artificial neural network. J Environ Chem Eng 5(3):2142–2150CrossRefGoogle Scholar
  43. Huang Y, Lan Y, Thomson SJ, Fang A, Hoffmann WC, Lacey RE (2010) Development of soft computing and applications in agricultural and biological engineering. Comput Electron Agric 71(2):107–127CrossRefGoogle Scholar
  44. Iliadis LS, Spartalis SI, Paschalidou AK, Kassomenos P (2007) Artificial neural network modelling of the surface ozone concentration. International. J Comput Appl Math 2(2):125–138Google Scholar
  45. Jang H, Topal E (2014) A review of soft computing technology applications in several mining problems. Appl Soft Comput 22:638–651CrossRefGoogle Scholar
  46. Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cyber 23(3):665–685CrossRefGoogle Scholar
  47. Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing. A computational approach to learning and machine intelligence. Prentice-Hall, Englewood CliffsGoogle Scholar
  48. Jantzen J (1999) Design of fuzzy controllers. Technical University of Denmark: technical report (No: 98–E864) Department of Automation, HobokenGoogle Scholar
  49. Kotti IP, Sylaios GK, Tsihrintzis VA (2013) Fuzzy logic models for BOD removal prediction in free-water surface constructed wetlands. Ecol Eng 51:66–74CrossRefGoogle Scholar
  50. Kurt A, Gulbagci B, Karaca F, Alagha O (2008) An online air pollution forecasting system using neural networks. Environ Int 34(5):592–598CrossRefGoogle Scholar
  51. Kusan H, Aytekin O, Özdemir İ (2010) The use of fuzzy logic in predicting house selling price. Expert Syst Appl 37(3):1808–1813CrossRefGoogle Scholar
  52. Lin KP, Pai PF, Yang SL (2011) Forecasting concentrations of air pollutants by logarithm support vector regression with immune algorithms. Appl Math Comput 217(12):5318–5327Google Scholar
  53. Liu Z, Meng X (2009) Integration of improved BPNN algorithm and multistage dynamic fuzzy judgement and its application on ESMP evaluation. J. Computers 4(1):69–76CrossRefGoogle Scholar
  54. Liu S, Tai H, Ding Q, Li D, Xu L, Wei Y (2013) A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction. Math Comput Model 58(3):458–465CrossRefGoogle Scholar
  55. Lou I, Xie Z, Ung WK, Mok KM (2017) Integrating support vector regression with particle swarm optimization for numerical modeling for algal blooms of freshwater. In Advances in monitoring and modelling algal blooms in freshwater reservoirs, Springer Netherlands, pp. 125–141Google Scholar
  56. Lu WZ, Wan WJ (2005) Potential assessment of the “support vector machine” method in forecasting ambient air pollutant trends. Chemosphere 59(5):693–701CrossRefGoogle Scholar
  57. Luna AS, Paredes MLL, de Oliveira GCG, Corrêa SM (2014) Prediction of ozone concentration in tropospheric levels using artificial neural networks and support vector machine at Rio de Janeiro, Brazil. Atmos Environ 98:98–104CrossRefGoogle Scholar
  58. Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7(1):1–13CrossRefGoogle Scholar
  59. Mandal S, Mahapatra SS, Patel RK (2015) Neuro fuzzy approach for arsenic (III) and chromium (VI) removal from water. J Water Process Eng 5:58–75CrossRefGoogle Scholar
  60. McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133CrossRefGoogle Scholar
  61. Metternicht G, Gonzalez S (2005) FUERO: foundations of a fuzzy exploratory model for soil erosion hazard prediction. Environ Model Softw 20(6):715–728CrossRefGoogle Scholar
  62. Mingzhi H, Ma Y, Jinquan W, Yan W (2009) Simulation of a paper mill wastewater treatment using a fuzzy neural network. Expert Syst Appl 36(3):5064–5070CrossRefGoogle Scholar
  63. Mishra D, Goyal P (2016) Neuro–Fuzzy approach to forecasting Ozone Episodes over the urban area of Delhi, India. Environ Technol Innov 5:83–94CrossRefGoogle Scholar
  64. Moazami S, Noori R, Amiri BJ, Yeganeh B, Partani S, Safavi S (2016) Reliable prediction of carbon monoxide using developed support vector machine. Atmos Pollut Res 7(3):412–418CrossRefGoogle Scholar
  65. Molga E, Cherbański R, Szpyrkowicz L (2006) Modeling of an industrial full-scale plant for biological treatment of textile wastewaters: application of neural networks. Ind Eng Chem Res 45(3):1039–1046CrossRefGoogle Scholar
  66. Mullai P, Arulselvi S, Ngo HH, Sabarathinam PL (2011) Experiments and ANFIS modelling for the biodegradation of penicillin–G wastewater using anaerobic hybrid reactor. Bioresour Technol 102(9):5492–5497CrossRefGoogle Scholar
  67. Murnleitner E, Becker TM, Delgado A (2002) State detection and control of overloads in the anaerobic wastewater treatment using fuzzy logic. Water Res 36(1):201–211CrossRefGoogle Scholar
  68. Nasiri F, Huang G (2008) A fuzzy decision aid model for environmental performance assessment in waste recycling. Environ Model Softw 23(6):677–689CrossRefGoogle Scholar
  69. Noori R, Hoshyaripour G, Ashrafi K, Araabi BN (2010) Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration. Atmos Environ 44(4):476–482CrossRefGoogle Scholar
  70. Noori R, Yeh HD, Abbasi M, Kachoosangi FT, Moazami S (2015) Uncertainty analysis of support vector machine for online prediction of five-day biochemical oxygen demand. J Hydrol 527:833–843CrossRefGoogle Scholar
  71. Nunnari G, Dorling S, Schlink U, Cawley G, Foxall R, Chatterton T (2004) Modelling SO2 concentration at a point with statistical approaches. Environ Model Softw 19(10):887–905CrossRefGoogle Scholar
  72. Oliveira-Esquerre KP, Mori M, Bruns RE (2002) Simulation of an industrial wastewater treatment plant using artificial neural networks and principal components analysis. Braz J Chem Eng 19(4):365–370CrossRefGoogle Scholar
  73. Olvera-García MÁ, Carbajal-Hernández JJ, Sánchez-Fernández LP, Hernández-Bautista I (2016) Air quality assessment using a weighted Fuzzy Inference System. Eco Inform 33:57–74CrossRefGoogle Scholar
  74. Osowski S, Garanty K (2007) Forecasting of the daily meteorological pollution using wavelets and support vector machine. Eng Appl Artif Intell 20(6):745–755CrossRefGoogle Scholar
  75. Ozcan F, Atiş CD, Karahan O, Uncuoğlu E, Tanyildizi H (2009) Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete. Adv Eng Softw 40(9):856–863CrossRefGoogle Scholar
  76. Ozdemir H, Demir G, Altay G, Albayrak S, Bayat C (2008) Prediction of tropospheric ozone concentration by employing artificial neural networks. Environ Eng Sci 25(9):1249–1254CrossRefGoogle Scholar
  77. Ozger M, Sen Z (2007) Prediction of wave parameters by using fuzzy logic approach. Ocean Eng 34(3):460–469CrossRefGoogle Scholar
  78. Ozkaya B, Demir A, Bilgili MS (2007) Neural network prediction model for the methane fraction in biogas from field-scale landfill bioreactors. Environ Model Softw 22(6):815–822CrossRefGoogle Scholar
  79. Ozkaya B, Sahinkaya E, Nurmi P, Kaksonen AH, Puhakka JA (2008) Biologically Fe2+ oxidizing fluidized bed reactor performance and controlling of Fe3+ recycle during heap bioleaching: an artificial neural network-based model. Bioprocess Biosyst Eng 31(2):111–117CrossRefGoogle Scholar
  80. Pai TY, Wan TJ, Hsu ST, Chang TC, Tsai YP, Lin CY, Su HC, Yu LF (2009) Using fuzzy inference system to improve neural network for predicting hospital wastewater treatment plant effluent. Comput Chem Eng 33(7):1272–1278CrossRefGoogle Scholar
  81. Pai TY, Yang PY, Wang SC, Lo MH, Chiang CF, Kuo JL, Chu HH, Su HC, Yu LF, Hu HC, Chang YH (2011) Predicting effluent from the wastewater treatment plant of industrial park based on fuzzy network and influent quality. Appl Math Model 35(8):3674–3684CrossRefGoogle Scholar
  82. Park S, Kim M, Kim M, Namgung HG, Kim KT, Cho KH, Kwon SB (2018) Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN). J Hazard Mater 341:75–82CrossRefGoogle Scholar
  83. Parveen N, Zaidi S, Danish M (2016) Support vector regression model for predicting the sorption capacity of lead (II). Perspect Sci 8:629–631CrossRefGoogle Scholar
  84. Pendashteh AR, Fakhru’l-Razi A, Chaibakhsh N, Abdullah LC, Madaeni SS, Abidin ZZ (2011) Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network. J Hazard Mater 192(2):568–575CrossRefGoogle Scholar
  85. Perendeci A, Arslan S, Çelebi SS, Tanyolaç A (2008) Prediction of effluent quality of an anaerobic treatment plant under unsteady state through ANFIS modeling with on-line input variables. Chem Eng J 145(1):78–85CrossRefGoogle Scholar
  86. Platt L (1998) Fast training of SVM using sequential optimization. In: Scholkopf B, Burges B, Smola A (eds) Advances in kernel methods – support vector learning. MIT Press, Cambridge, pp 185–208Google Scholar
  87. Podder MS, Majumder CB (2016) Phycoremediation potential of Botryococcus braunii. Water Conserv Sci Eng 1(1):49–68CrossRefGoogle Scholar
  88. Prasad K, Gorai AK, Goyal P (2016) Development of ANFIS models for air quality forecasting and input optimization for reducing the computational cost and time. Atmos Environ 128:246–262CrossRefGoogle Scholar
  89. Qaderi F, Babanejad E (2017) Prediction of the groundwater remediation costs for drinking use based on quality of water resource, using artificial neural network. J Clean Prod 161:840–849CrossRefGoogle Scholar
  90. Rangasamy P, Iyer PVR, Ganesan S (2007) Anaerobic tapered fluidized bed reactor for starch wastewater treatment and modeling using multilayer perceptron neural network. J Environ Sci 19(12):1416–1423CrossRefGoogle Scholar
  91. Raduly B, Gernaey KV, Capodaglio AG, Mikkelsen PS, Henze M (2007) Artificial neural networks for rapid WWTP performance evaluation: methodology and case study. Environ Model Softw 22(8):1208–1216CrossRefGoogle Scholar
  92. Rahimzadeh A, Ashtiani FZ, Okhovat A (2016) Application of adaptive neuro-fuzzy inference system as a reliable approach for prediction of oily wastewater microfiltration permeate volume. J Environ Chem Eng 4(1):576–584CrossRefGoogle Scholar
  93. Rihani R, Bensmaili A, Legrand J (2009) Fuzzy logic modelling tracer response in milli torus reactor under aerated and non-aerated conditions. Chem Eng J 152(2):566–574CrossRefGoogle Scholar
  94. Rubens N (2006) The application of fuzzy logic to the construction of the ranking function of information retrieval systems. Comput Model New Technol 10:20–27Google Scholar
  95. Sadiq R, Al-Zahrani MA, Sheikh AK, Husain T, Farooq S (2004) Performance evaluation of slow sand filters using fuzzy rule-based modelling. Environ Model Softw 19(5):507–515CrossRefGoogle Scholar
  96. Sadrzadeh M, Ghadimi A, Mohammadi T (2009) Coupling a mathematical and a fuzzy logic-based model for prediction of zinc ions separation from wastewater using electrodialysis. Chem Eng J 151(1):262–274CrossRefGoogle Scholar
  97. Sahinkaya E, Özkaya B, Kaksonen AH, Puhakka JA (2007) Neural network prediction of thermophilic (65°C) sulfidogenic fluidized-bed reactor performance for the treatment of metal-containing wastewater. Biotechnol Bioeng 97(4):780–787CrossRefGoogle Scholar
  98. Sahinkaya E (2009) Biotreatment of zinc-containing wastewater in a sulfidogenic CSTR: performance and artificial neural network (ANN) modelling studies. J Hazard Mater 164(1):105–113CrossRefGoogle Scholar
  99. Saral A, Ertürk F (2003) Prediction of ground level SO2 concentration using artificial neural networks. Water Air Soil Pollut Focus 3(5):307–316CrossRefGoogle Scholar
  100. Sari H, Yetilmezsoy K, Ilhan F, Yazici S, Kurt U, Apaydin O (2013) Fuzzy-logic modeling of Fenton’s strong chemical oxidation process treating three types of landfill leachates. Environ Sci Pollut Res 20(6):4235–4253CrossRefGoogle Scholar
  101. Schölkopf B, Smola AJ (2002) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge, MAGoogle Scholar
  102. Shahraiyni HT, Sodoudi S, Kerschbaumer A, Cubasch U (2015) A new structure identification scheme for ANFIS and its application for the simulation of virtual air pollution monitoring stations in urban areas. Eng Appl Artif Intell 41:175–182CrossRefGoogle Scholar
  103. Singh KP, Basant N, Gupta S (2011) Support vector machines in water quality management. Anal Chim Acta 703(2):152–162CrossRefGoogle Scholar
  104. Sofuoglu SC, Sofuoglu A, Birgili S, Tayfur G (2006) Forecasting ambient air SO2 concentrations using artificial neural networks. Energy Sources Part B 1(2):127–136CrossRefGoogle Scholar
  105. Sowlat MH, Gharibi H, Yunesian M, Mahmoudi MT, Lotfi S (2011) A novel, fuzzy-based air quality index (FAQI) for air quality assessment. Atmos Environ 45(12):2050–2059CrossRefGoogle Scholar
  106. Sozen A, Kurt M, Akçayol MA, Özalp M (2004) Performance prediction of a solar driven ejector-absorption cycle using fuzzy logic. Renew Energy 29(1):53–71CrossRefGoogle Scholar
  107. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 1:116–132CrossRefGoogle Scholar
  108. Tay JH, Zhang X (2000) A fast predicting neural fuzzy model for high-rate anaerobic wastewater treatment systems. Water Res 34(11):2849–2860CrossRefGoogle Scholar
  109. Taylan O (2017) Modelling and analysis of ozone concentration by artificial intelligent techniques for estimating air quality. Atmos Environ 150:356–365CrossRefGoogle Scholar
  110. Topcu İB, Saridemir M (2008) Prediction of rubberized concrete properties using artificial neural network and fuzzy logic. Constr Build Mater 22(4):532–540CrossRefGoogle Scholar
  111. Traore A, Grieu S, Puig S, Corominas L, Thiéry F, Polit M, Colprim J (2005) Fuzzy control of dissolved oxygen in a sequencing batch reactor pilot plant. Chem Eng J 111(1):13–19CrossRefGoogle Scholar
  112. Turkdogan-Aydınol FI, Yetilmezsoy K (2010) A fuzzy-logic-based model to predict biogas and methane production rates in a pilot-scale mesophilic UASB reactor treating molasses wastewater. J Hazard Mater 182(1):460–471CrossRefGoogle Scholar
  113. Vakili M, Sabbagh-Yazdi SR, Kalhor K, Khosrojerdi S (2015) Using artificial neural networks for prediction of global solar radiation in Tehran considering particulate matter air pollution. Energy Procedia 74:1205–1212CrossRefGoogle Scholar
  114. Vapnik V (1995) Nature of statistical learning theory. Springer, New YorkCrossRefGoogle Scholar
  115. Vapnik V (1998) Statistical learning theory. Wiley, New YorkGoogle Scholar
  116. Wan J, Huang M, Ma Y, Guo W, Wang Y, Zhang H, Li W, Sun X (2011) Prediction of effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy inference system. Appl Soft Comput 11(3):3238–3246CrossRefGoogle Scholar
  117. Wieland D, Wotawa F, Wotawa G (2002) From neural networks to qualitative models in environmental engineering. Comput Aided Civ Infrastruct Eng 17(2):104–118CrossRefGoogle Scholar
  118. Wotawa F, Wotawa G (2001) Deriving qualitative rules from neural networks – a case study for ozone forecasting. AI Commun 14(1):23–33Google Scholar
  119. Wu GD, Lo SL (2008) Predicting real-time coagulant dosage in water treatment by artificial neural networks and adaptive network-based fuzzy inference system. Eng Appl Artif Intell 21(8):1189–1195CrossRefGoogle Scholar
  120. Xie Q, Ni JQ, Su Z (2017) A prediction model of ammonia emission from a fattening pig room based on the indoor concentration using adaptive neuro fuzzy inference system. J Hazard Mater 325:301–309CrossRefGoogle Scholar
  121. Xu Y, Ma C, Liu Q, Xi B, Qian G, Zhang D, Huo S (2015) Method to predict key factors affecting lake eutrophication – a new approach based on Support Vector Regression model. Int Biodeterior Biodegrad 102:308–315CrossRefGoogle Scholar
  122. Yeganeh B, Motlagh MSP, Rashidi Y, Kamalan H (2012) Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model. Atmos Environ 55:357–365CrossRefGoogle Scholar
  123. Yetilmezsoy K (2006) Determination of optimum body diameter of air cyclones using a new empirical model and a neural network approach. Environ Eng Sci 23(4):680–690CrossRefGoogle Scholar
  124. Yetilmezsoy K (2012) Fuzzy-logic modeling of Fenton’s oxidation of anaerobically pretreated poultry manure wastewater. Environ Sci Pollut Res 19(6):2227–2237CrossRefGoogle Scholar
  125. Yetilmezsoy K, Abdul-Wahab SA (2012) A prognostic approach based on fuzzy-logic methodology to forecast PM10 levels in Khaldiya residential area, Kuwait. Aerosol Air Qual Res 12(6):1217–1236Google Scholar
  126. Yetilmezsoy K, Demirel S (2008) Artificial neural network (ANN) approach for modeling of Pb (II) adsorption from aqueous solution by Antep pistachio (Pistacia Vera L.) shells. J Hazard Mater 153(3):1288–1300CrossRefGoogle Scholar
  127. Yetilmezsoy K, Sapci-Zengin Z (2009) Stochastic modeling applications for the prediction of COD removal efficiency of UASB reactors treating diluted real cotton textile wastewater. Stoch Environ Res Risk Assess 23(1): 13–26.CrossRefGoogle Scholar
  128. Yetilmezsoy K, Saral A (2007) Stochastic modeling approaches based on neural network and linear–nonlinear regression techniques for the determination of single droplet collection efficiency of countercurrent spray towers. Environ Model Assess 12(1):13–26CrossRefGoogle Scholar
  129. Yetilmezsoy K (2010) Modeling studies for the determination of completely mixed activated sludge reactor volume: Steady-state, empirical and ANN applications. Neural Netw World 20(5): 559–589.Google Scholar
  130. Yetilmezsoy K, Ozkaya B, Cakmakci M (2011a) Artificial intelligence-based prediction models for environmental engineering. Neural Netw World 21(3):193–218CrossRefGoogle Scholar
  131. Yetilmezsoy K, Fingas M, Fieldhouse B (2011b) An adaptive neuro-fuzzy approach for modeling of water-in-oil emulsion formation. Colloids Surf A Physicochem Eng Asp 389(1):50–62CrossRefGoogle Scholar
  132. Yetilmezsoy K, Fingas M, Fieldhouse B (2012) Modeling water-in-oil emulsion formation using fuzzy logic. J Mult Valued Log Soft Comput 18:329–353Google Scholar
  133. Yetilmezsoy K, Turkdogan FI, Temizel I, Gunay A (2013) Development of ann-based models to predict biogas and methane productions in anaerobic treatment of molasses wastewater. Int J Green Energy 10(9):885–907CrossRefGoogle Scholar
  134. Yetilmezsoy K, Ozgun H, Dereli RK, Ersahin ME, Ozturk I (2015) Adaptive neuro-fuzzy inference-based modeling of a full-scale expanded granular sludge bed reactor treating corn processing wastewater. J Intell Fuzzy Syst 28(4):1601–1616Google Scholar
  135. Yildirim Y, Bayramoglu M (2006) Adaptive neuro-fuzzy based modelling for prediction of air pollution daily levels in city of Zonguldak. Chemosphere 63(9):1575–1582CrossRefGoogle Scholar
  136. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353CrossRefGoogle Scholar
  137. Zakaria Z, Isa NAM, Suandi SA (2010) A study on neural network training algorithm for multiface detection in static images. World Acad Sci Eng Techn 4(2):345–348Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Environmental Engineering, Faculty of Civil EngineeringYildiz Technical UniversityIstanbulTurkey

Section editors and affiliations

  • Chaudhery Mustansar Hussain
    • 1
  1. 1.Department of Chemistry and Environmental SciencesNew Jersey Institute of TechnologyNewarkUSA

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