References

  1. L. Li, P. Jiang, H. Xu, G. Lin, D. Guo, H. Wu, Water quality prediction based on recurrent neural network and improved evidence theory: a case study of Qiantang River, Environ. Sci. Pollut. Res., 26 (2019) 879–896.
  2. M. Homami, S.A. Mirbagheri, S.M. Borghei, M. Abbaspour, Simulation modeling of nutrients, dissolved oxygen and total dissolved solids in Peer-Bazar River and Anzali wetland eutrophication prediction, Desal. Water Treat., 79 (2017) 108–124.
  3. O.T. Baki, E. Aras, U.O. Akdemir, B. Yilmaz, Biochemical oxygen demand prediction in wastewater treatment plant by using different regression analysis models, Desal. Water Treat., 157 (2019) 79–89.
  4. S.Y. Liu, H.J. Tai, Q.S. Ding, D.L. Li, L.Q. Xu, Y.G. Wei, A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction, Math. Comput. Modell., 58 (2013) 458–465.
  5. J.J. Carbajal-Hernandez, L.P. Sanchez-Fernandez, L.A. Villa- Vargas, J.A. Carrasco-Ochoa, J.F. Martínez-Trinidad, Water quality assessment in shrimp culture using an analytical hierarchical process. Ecol. Indic., 29 (2013) 148–158.
  6. S.A. Dellana, D. West, Predictive modeling for wastewater applications: linear and nonlinear approaches, Environ. Modell. Softw., 24 (2009) 96–106.
  7. E.V. Hatzikos, G. Tsoumakas, G. Tzanis, An empirical study on sea water quality prediction, Knowledge-Based Syst., 21 (2008) 471–478.
  8. H. Amdevyren, N. Demyr, A. Kanik, Use of principal component scores in multiple linear regression models for prediction of Chlorophyll-a in reservoirs, Ecol. Modell., 181 (2005) 581–589.
  9. X. Ta, Y. Wei, Research on a dissolved oxygen prediction method for recirculating aquaculture systems based on a convolution neural network, Comput. Electron. Agric., 145 (2018) 302–310.
  10. S. Palani, S.Y. Liong, P. Tkalich, An ANN application for water quality forecasting, Mar. Pollut. Bull., 56 (2008) 1586–1597.
  11. J.H. Cho, S.K. Seok, H.S. Ryong, A river water quality management model for optimizing regional waster treatment using a genetic algorithm, J. Environ. Manage., 73 (2004) 229–242.
  12. H.G. Han, Q.L. Chen, J.F. Qiao, An efficient self-organizing RBF neural network for water quality prediction, Neural Networks, 24 (2001) 717–725.
  13. Y.R. Ding, Y.J. Cai, P.D. Sun, B. Chen, The use of combined neural networks and genetic algorithms for prediction of river water quality, J. Appl. Res. Technol., 12 (2004) 493–499.
  14. M. Mahmoodabadi, R.R. Arshad, Long-term evaluation of water quality parameters of the Karoun River using a regression approach and the adaptive neuro-fuzzy inference system, Mar. Pollut. Bull., 126 (2018) 372–380.
  15. H.M. Lee, C.M. Chen, T.C. Huang, Learning efficiency improvement of back-propagation algorithm by error saturation prevention method, Neurocomputing, 41 (2001) 125–143.
  16. H.C. Neiad, M. Farshad, F.N. Rahatabad, O. Khayat, Gradientbased back- propagation dynamical iterative learning scheme for the neuro-fuzzy inference system, Expert Syst., 33 (2016) 70–76.
  17. B. Scholkopf, A.J. Smola, R. Williamson, P. Bartlett, New support vector algorithms, Neural Comput., 12 (2000) 1207–1245.
  18. T. Hansen, C.J. Wang, Support vector based battery state of charge estimator, J. Power Sources, 141 (2005) 351–358.
  19. X. Li, D. Lord, Y. Zhang, Y. Xie, Predicting motor vehicle crashes using Support Vector Machine models, Accid. Anal. Prev., 40 (2008) 1611–1618.
  20. F. Nagata, K. Tokuno, K. Mitarai, Defect detection method using deep convolutional neural network, support vector machine and template matching techniques, Artif. Life Rob., 24 (2019) 512–519.
  21. V.H. Quej, J. Almorox, J.A. Arnaldo, L. Saito, ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment, J. Atmos. Sol. Terr. Phys., 155 (2017) 62–70.
  22. P.J. Garcia Nieto, J. Martinez Torres, M. Araujo Fernandez, C. Ordonez Galan, Support vector machines and neural networks used to evaluate paper manufactured using Eucalyptus globulus, Appl. Math. Modell., 36 (2012) 6137–6145.
  23. A. Suarez Sanchez, P.J. Garcia Nieto, P. Riesgo Fernandez, F.J. Iglesias Rodriguez, Application of an SVM-based regression model to the air quality study at local scale in the Aviles urban area (Spain), Math. Comput. Modell., 54 (2011) 1453–1466.
  24. P.J. Garcia Nieto, E.F. Combarro, J.J. del Coz Diaz, E. Montanes, A SVM-based regression model to study the air quality at local scale in Oviedo urban area (Northern Spain): a case study, Appl. Math. Comput., 219 (2013) 8923–8937.
  25. P.J. García Nieto, E. Garcia Gonzalo, J.R. Alonso Fernandez, C. Diaz Muniz, Hybrid PSO–SVM-based method for longterm forecasting of turbidity in the Nalon river basin: a case study in Northern Spain, Ecol. Eng., 73 (2014) 192–200.
  26. Y.G. Oh, M. Busogi, K. Ransikarbum, D. Shin, D. Kwon, N. Kim, Real-time quality monitoring and control system using an integrated cost effective support vector machine, J. Mech. Sci Technol., 33 (2019) 6008–6020.
  27. S. Kulkarni, G. Harman, An Elementary Introduction to Statistical Learning Theory, Wiley, New York, NY, 2011.
  28. S. Mirjalili, S.N. Mirjalili, A. Lewis, Grey wolf optimizer, Adv. Eng. Softw., 69 (2014) 46–61.
  29. B. Xiao Qiang, Z. Lu, D. Zhi Min, C. Jing, Z. Jian Ye, Prediction of sulfur solubility in supercritical sour gases using grey wolf optimizer-based support vector machine, J. Mol. Liq., 261 (2018) 431–438.
  30. L.Z. Cui, G.H. Li, Z.X. Zhu, Z.K. Wen, N. Lu, J. Lu, A novel differential evolution algorithm with a self-adaptation parameter control method by differential evolution, Soft Comput., 22 (2018) 6171–6190.
  31. J.S. Chou, C.P. Yu, D.N. Truong, B. Susilo, A.Y. Hu, Q. Sun, Predicting microbial species in a river based on physicochemical properties by bio-inspired metaheuristic optimized machine learning, Sustainability, 11 (2019), doi: 10.3390/su11246889.
  32. S.H. Wang, Y. Li, Y. Shao, C. Cattani, Y.D. Zhang, S.D. Du, Detection of dendritic spines using wavelet packet entropy and fuzzy support vector machine, CNS Neurol. Disord. Drug Targets, 16 (2017) 116–121.
  33. H.W. Liu, K. Guo, Z.Y. Zhang, D.D. Yu, J.X. Zhang, F.P. Ning, High-power LED photoelectrothermal analysis based on backpropagation artificial neural networks, IEEE Trans. Electron Devices, 64 (2017) 2867–2873.
  34. W.Z. Lu, W.J. Wang, Potential assessment of the “support vector machine” method in forecasting ambient air pollutant trends, Chemosphere, 59 (2005) 693–701.
  35. X.P. Liao, G. Zhou, Z.K. Zhang, J. Lu, J.Y. Ma, Tool wear state recognition based on GWO-SVM with feature selection of genetic algorithm, Int. J. Adv. Manuf. Technol., 104 (2019) 1051–1063.
  36. N.M. Hatta, A.M. Zain, R. Sallehuddin, Z. Shayfull, Y. Yusoff, Recent studies on optimisation method of Grey Wolf Optimiser (GWO): a review (2014–2017), Artif. Intell. Rev., 52 (2019) 2651–2683.
  37. A. Korashy, S. Kamel, F. Jurado, A.R. Youssef, Hybrid whale optimization algorithm and grey wolf optimizer algorithm for optimal coordination of direction overcurrent relays, Electr. Power Compon. Syst., 47 (2019) 644–658.