1. WHO, UNCF (UNICEF), Progress on Sanitation and Drinking Water: Joint Monitoring Programme 2010 Update, World Health Organization, United Nations Children’s Fund, WHO Press, Geneva, Switzerland, 2012.
  2. T. Gómez, G. Gémar, M. Molinos-Senante, R. Sala-Garrido, R. Caballero, Assessing the efficiency of wastewater treatment plants: a double-bootstrap approach, J. Cleaner Prod., 164 (2017) 315–324.
  3. V. Nourani, G. Elkiran, S.I. Abba, Wastewater treatment plant performance analysis using artificial intelligence – an ensemble approach, Water Sci. Technol., 78 (2018) 2064–2076.
  4. S.I. Abba, G. Elkiran, Effluent prediction of chemical oxygen demand from the wastewater treatment plant using artificial neural network application, Procedia Comput. Sci., 120 (2017) 156–163.
  5. N. Bekkari, A. Zeddouri, Using artificial neural network for predicting and controlling the effluent chemical oxygen demand in wastewater treatment plant, Manage. Environ. Qual. Int. J., 30 (2019) 593–608.
  6. X.D. Wang, K. Kvaal, H. Ratnaweera, Explicit and interpretable nonlinear soft sensor models for influent surveillance at a fullscale wastewater treatment plant, J. Process Control, 77 (2019) 1–6.
  7. S.R. Naganna, P.C. Deka, M.A. Ghorbani, S.M. Biazar, N. Al-Ansari, Z.M. Yaseen, Dew Point temperature estimation: application of artificial intelligence model integrated with nature-inspired optimization algorithms, Water (Switzerland), 11 (2019) 1–17.
  8. S.I. Abba, Q.B. Pham, G. Saini, N.T. Linh, A.N. Ahmed, M. Mohajane, M. Khaledian, R.A. Abdulkadir, Q.-V. Bach, Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index, Environ. Sci. Pollut. Res., 27 (2020) 41524–41539.
  9. G. Elkiran, V. Nourani, S.I. Abba, J. Abdullahi, Artificial intelligence-based approaches for multi-station modeling of dissolve oxygen in river, Global J. Environ. Sci. Manage., 4 (2018) 439–450.
  10. V. Nourani, G. Andalib, F. Sadikoglu, Multi-station streamflow forecasting using wavelet denoising and artificial intelligence models, Procedia Comput. Sci., 120 (2017) 617–624.
  11. Y.Y. Zhang, X. Gao, K. Smith, G. Inial, S.M. Liu, L.B. Conil, B.C. Pan, Integrating water quality and operation into prediction of water production in drinking water treatment plants by genetic algorithm enhanced artificial neural network, Water Res., 164 (2019) 114888, watres.2019.114888.
  12. G. Elkiran, V. Nourani, S.I. Abba, Multi-step ahead modeling of river water quality parameters using ensemble artificial intelligence-based approach, J. Hydrol., 577 (2019) 123962,
  13. V. Nourani, N. Farboudfam, Rainfall time series disaggregation in mountainous regions using hybrid wavelet-artificial intelligence methods, Environ. Res., 168 (2019) 306–318.
  14. A. Maleki, S. Nasseri, M.S. Aminabad, M. Hadi, Comparison of ARIMA and NNAR models for forecasting water treatment plant’s influent characteristics, KSCE J. Civ. Eng., 22 (2018) 3233–3245.
  15. W.C. Chen, N.-B. Chang, W.K. Shieh, Advanced hybrid fuzzy-neural controller for industrial wastewater treatment, J. Environ. Eng., 127 (2001) 1048–1059.
  16. F. Granata, S. Papirio, G. Esposito, R. Gargano, G. de Marinis, Machine learning algorithms for the forecasting of wastewater quality indicators, Water (Switzerland), 9 (2017) 1–12.
  17. A.K. Verma, T.N. Singh, Prediction of water quality from simple field parameters, Environ. Earth Sci., 69 (2013) 821–829.
  18. H. Guo, K.H. Jeong, J.Y. Lim, J.W. 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.
  19. S.I. Abba, V. Nourani, G. Elkiran, Multi-parametric modeling of water treatment plant using AI-based non-linear ensemble, J. Water Supply Res. Technol. AQUA, 68 (2019) 547–561.
  20. S.I. Abba, G. Elkiran, V. Nourani, Non-linear Ensemble Modeling for Multi-step Ahead Prediction of Treated COD in Wastewater Treatment Plant, R.A. Aliev, J. Kacprzyk, W. Pedrycz, M. Jamshidi, M.B. Babanli, F.M. Sadikoglu, Eds., 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions – ICSCCW-2019, Advances in Intelligent Systems and Computing, Vol. 1095, Springer, Cham, 2020, pp. 683–689.
  21. G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, Extreme learning machine: theory and applications, Neurocomputing, 70 (2006) 489–501.
  22. A. Solgi, A. Pourhaghi, R. Bahmani, H. Zarei, Improving SVR and ANFIS performance using wavelet transform and PCA algorithm for modeling and predicting biochemical oxygen demand (BOD), Ecohydrol. Hydrobiol., 17 (2017) 164–175.
  23. Q. Wang, Kernel principal component analysis and its applications in face recognition and active shape models, Comput. Vision Pattern Recognit., (2012) arXiv:1207.3538.
  24. S.I. Abba, Q.B. Pham, A.G. Usman, N.T. Thuy Linh, D.S. Aliyu, Q. Nguyen, Q.-V. Bach, Emerging evolutionary algorithm integrated with kernel principal component analysis for modeling the performance of a water treatment plant, J. Water Process Eng., 33 (2020) 101081, jwpe.2019.101081.
  25. M. Yaqub, H. Asif, S.B. Kim, W.T. Lee, Modeling of a fullscale sewage treatment plant to predict the nutrient removal efficiency using a long short-term memory (LSTM) neural network, J. Water Process Eng., 37 (2020) 101388, https://doi. org/10.1016/j.jwpe.2020.101388.
  26. J.-H. Kang, J.H. Song, S.S. Yoo, B.-J. Lee, H.W. Ji, Prediction of odor concentration emitted from wastewater treatment plant using an artificial neural network (ANN), Atmosphere (Basel), 11 (2020) 784,
  27. M. Ansari, F. Othman, A. El-Shafie, Optimized fuzzy inference system to enhance prediction accuracy for influent characteristics of a sewage treatment plant, Sci. Total Environ., 722 (2020) 137878, doi: 10.1016/j.scitotenv.2020.137878.
  28. A.M. Anter, D. Gupta, O. Castillo, A novel parameter estimation in dynamic model via fuzzy swarm intelligence and chaos theory for faults in wastewater treatment plant, Soft Comput., 24 (2020) 111–129.
  29. N. Patel, J. Ruparelia, J. Barve, Prediction of total suspended solids present in effluent of primary clarifier of industrial common effluent treatment plant: mechanistic and fuzzy approach, J. Water Process Eng., 34 (2020) 101146, https://doi. org/10.1016/j.jwpe.2020.101146.
  30. A. Sharafati, S.B.H.S. Asadollah, M. Hosseinzadeh, The potential of new ensemble machine learning models for effluent quality parameters prediction and related uncertainty, Process Saf. Environ. Prot., 140 (2020) 68–78.
  31. UNDP, New Nicosia Waste Water Treatment Plant, United Nations Development Programme, Nicosia, Northern Part of Cyprus, 2014.
  32. P. Shi, G.H. Li, Y.M. Yuan, G.Y. Huang, L. Kuang, Prediction of dissolved oxygen content in aquaculture using clustering-based softplus extreme learning machine, Comput. Electron. Agric., 157 (2019) 329–338.
  33. G. Huang, G.-B. Huang, S.J. Song, K.Y. You, Trends in extreme learning machines: a review, Neural Networks, 61 (2015) 32–48.
  34. Z.M. Yaseen, S.O. Sulaiman, R.C. Deo, K.-W. Chau, An enhanced extreme learning machine model for river flow forecasting: state-of-the-art, practical applications in water resource engineering area and future research direction, J. Hydrol., 569 (2018) 387–408.
  35. S.J. Hadi, S.I. Abba, S.S. Sammen, S.Q. Salih, N. Al-Ansari, Z.M. Yaseen, Non-linear input variable selection approach integrated with non-tuned data intelligence model for streamflow pattern simulation, IEEE Access, 7 (2019) 141533–141548.
  36. S. Zhu, S. Heddam, Prediction of dissolved oxygen in urban rivers at the three Gorges reservoir, China: extreme learning machines (ELM) versus artificial neural network (ANN), Water Qual. Res. J. Canada, 55 (2020) 106–118.
  37. H. Chen, Q. Zhang, J. Luo, Y. Xu, X. Zhang, An enhanced Bacterial Foraging Optimization and its application for training kernel extreme learning machine, Appl. Soft Comput., 86 (2020) 105884.
  38. S. Heddam, O. Kisi, Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors, Environ. Sci. Pollut. Res., 24 (2017) 16702–16724.
  39. J. Jin, P. Jiang, L. Li, H. Xu, G. Lin, Water quality monitoring at a virtual watershed monitoring station using a modified deep extreme learning machine, Hydrol. Sci. J., 65 (2020) 415–426.
  40. Z.M. Yaseen, H. Faris, N. Al-Ansari, Hybridized extreme learning machine model with salp swarm algorithm: a novel predictive model for hydrological application, Complexity, 2020 (2020), doi: 10.1155/2020/8206245.
  41. Q.B. Pham, S.I. Abba, A.G. Usman, N.T.T. Linh, V. Gupta, A. Malik, R. Costache, N.D. Vo, D.Q. Tri, Potential of hybrid data-intelligence algorithms for multi-station modeling of rainfall, Water Resour. Manage., 33 (2019) 5067–5087.
  42. M.A. Ghorbani, R.C. Deo, Z.M. Yaseen, M.H. Kashani, B. Mohammadi, Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran, Theor. Appl. Climatol., 133 (2018) 1119–1131.
  43. S.L. Zhu, S. Heddam, E.K. Nyarko, M. Hadzima-Nyarko, S. Piccolroaz, S.Q. Wu, Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models, Environ. Sci. Pollut. Res., 26 (2019) 402–420.
  44. A.G. Usman, S. Işik, S.I. Abba, A novel multi-model data-driven ensemble technique for the prediction of retention factor in HPLC method development, Chromatographia, 83 (2020) 933–945.
  45. S.I. Abba, A.G. Usman, S. Işik, Simulation for response surface in the HPLC optimization method development using artificial intelligence models: a data-driven approach, Chemom. Intell. Lab. Syst., 201 (2020) 104007, chemolab.2020.104007.
  46. H.U. Abdullahi, A.G. Usman, S.I. Abba, Modeling the absorbance of a bioactive compound in HPLC method using artificial neural network and multilinear regression methods, Dutse J. Pure Appl. Sci., 6 (2020) 362–371.
  47. S.I. Abba, S.J. Hadi, S.S. Sammen, S.Q. Salih, R.A. Abdulkadir, Q.B. Pham, Z.M. Yaseen, Evolutionary computational intelligence algorithm coupled with self-tuning predictive model for water quality index determination, J. Hydrol., 587 (2020) 124974, doi: 10.1016/j.jhydrol.2020.124974.
  48. T.T. Yu, S. Yang, Y. Bai, X. Gao, C. Li, Inlet water quality forecasting of wastewater treatment based on kernel principal component analysis and an extreme learning machine, Water (Switzerland), 10 (2018) 873, doi: 10.3390/w10070873.
  49. M. Noori, R. Abdoli, M.A. Ghasrodashti, A.A. Ghasrodashti, J. Ghazizade, Prediction of municipal solid waste generation with combination of support vector machine and principal component analysis: a case study of Mashhad, Environ. Prog. Sustainable Energy, 28 (2009) 249–258.
  50. S.M. Holland, Principal components analysis (PCA), Dep. Geol. Univ. Georg. Athens, GA, 2008, pp. 30602–32501.
  51. J. Yang, G.W. Xu, H.W. Kong, Y.F. Zheng, T. Pang, Q. Yang, Artificial neural network classification based on highperformance liquid chromatography of urinary and serum nucleosides for the clinical diagnosis of cancer, J. Chromatogr. B, 780 (2002) 27–33.
  52. W.-Z. Lu, W.-J. Wang, X.-K. Wang, S.-H. Yan, J.C. Lam, Potential assessment of a neural network model with PCA/RBF approach for forecasting pollutant trends in Mong Kok urban air, Hong Kong, Environ. Res., 96 (2004) 79–87.
  53. M.S. Gaya, M.U. Zango, L.A. Yusuf, M. Mustapha, B. Muhammad, A. Sani, A. Tijjani, N.A. Wahab, M.T. Khairi, Estimation of turbidity in water treatment plant using Hammerstein-Wiener and neural network technique, Indonesian J. Electr. Eng. Comput. Sci., 5 (2017) 666–672.
  54. M.S. Gaya, S.I. Abba, A.M. Abdu, A.I. Tukur, M.A. Saleh, P. Esmaili, N.A. Wahab, Estimation of water quality index using artificial intelligence approaches and multi-linear regression, IAES Int. J. Artif. Intell., (2020) 8938, doi: 10.11591/ ijai.v9.i1.pp126-134.
  55. S.W. Kim, V.P. Singh, Modeling daily soil temperature using data-driven models and spatial distribution, Theor. Appl. Climatol., 118 (2014) 465–479.
  56. B. Mohammadi, N.T.T. Linh, Q.B. Pham, A.N. Ahmed, J. Vojteková, Y. Guan, A. El-Shafie, Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series, Hydrol. Sci. J., (2020) (In Press).
  57. D.R. Legates, G.J. McCabe Jr., Evaluating the use of “goodness of fit” measures in hydrologic and hydroclimatic model validation, Water Resour. Res., 35 (1999) 233–241.
  58. M. Alas, S.I.A. Ali, Y. Abdulhadi, S.I. Abba, Experimental evaluation and modeling of polymer nanocomposite modified asphalt binder using ANN and ANFIS, J. Mater. Civ. Eng., 32 (2020) 04020305.
  59. R.A. Abdulkadir, S.I.A. Ali, S.I. Abba, P. Esmaili, Forecasting of daily rainfall at Ercan Airport Northern Cyprus: a comparison of linear and non-linear models, Desal. Water Treat., 177 (2020) 297–305.
  60. S.I. Abba, N.T. Linh, J. Abdullahi, S.I. Ali, Q.B. Pham, R.A. Abdulkadir, R. Costache, D.T. Anh, Hybrid machine learning ensemble techniques for modeling dissolved oxygen concentration, IEEE Access, 8 (2020) 157218–157237.
  61. Q.B. Pham, M.S. Gaya, S.I. Abba, R.A. Abdulkadir, P. Esmaili, N.T. Linh, C. Sharma, A. Malik, D.N. Khoi, Modeling of Bunus regional sewage treatment plant using machine learning approaches, Desal. Water Treat., 203 (2020) 80–90.