References

1. G. Elkiran, A. Turkman, Water scarcity impacts on Northern Cyprus and alternative mitigation strategies, Provided for noncommercial research and education use. Not for reproduction, distribution or commercial use, January 2016, (2008), doi: 10.1007/978-1-4020-8960-2.
2. M.S. Gaya, N. Abdul Wahab, S.I. Samsudin, ANFIS modelling of carbon and nitrogen removal in domestic wastewater treatment plant, J. Technol., 67 (2014) 29–34.
3. Q.B. Pham, M.S. Gaya, S.I. Abba, R.A. Abdulkadir, P. Esmaili, N.T.H. Linh, C. Sharma, A. Malik, D.N. Khoi, D.D. Tran, L. Do, Modeling of Bunus regional sewage treatment plant using machine learning approaches, Desal. Water Treat., 203 (2020) 80–90.
4. N.A. Memon, M.A. Unar, A.K. Ansari, pH prediction by artificial neural networks for the drinking water of the distribution system of Hyderabad City, Neural Evol. Comput., 31 (2012) 137–146.
5. S.I. Abba, Q.B. Pham, G. Saini, N.T.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.
6. S.J. Hadi, M. Tombul, Forecasting daily streamflow for basins with different physical characteristics through data-driven methods, Water Resour. Manage., 32 (2018) 3405–3422.
7. S.I. Abba, G. Elkiran, V. Nourani, Nonlinear Ensemble Modeling for Multi-step Ahead Prediction of Treated COD in Wastewater Treatment Plant, R. Aliev, J. Kacprzyk, W. Pedrycz, M. Jamshidi, M. Babanli, F. 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, 2019, pp. 683–689. Available at: https://doi. org/10.1007/978-3-030-35249-3_88.
8. A.Š. Tomić, D. Antanasijević, M. Ristić, P.-G. Aleksandra, V. Pocajt, A linear and nonlinear polynomial neural network modeling of dissolved oxygen content in surface water: interand extrapolation performance with inputs’ significance analysis, Sci. Total Environ., 610–611 (2018) 1038–1046.
9. E. Sharghi, V. Nourani, A. Molajou, H. Najafi, Conjunction of emotional ANN (EANN) and wavelet transform for rainfallrunoff modeling, J. Hydroinf., 21 (2019) 136–152.
10. V. Nourani, A.H. Baghanam, J. Adamowski, O. Kisi, Applications of hybrid wavelet-artificial intelligence models in hydrology: a review, J. Hydrol., 514 (2014) 358–377.
11. T. Dede, M. Kankal, A.R. Vosoughi, M. Grzywiński, M. Kripka, Artificial intelligence applications in civil engineering, Adv. Civ. Eng., 2019 (2019) 8384523 (1–4), doi: 10.1155/2019/8384523.
12. G. Elkiran, V. Nourani, S.I. Abba, J. Abdullahi, Artificial intelligence-based approaches for multi-station modelling of dissolve oxygen in river, Global J. Environ. Sci. Manage., 4 (2018) 439–450.
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. 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.
15. M. Alizamir, O. Kisi, Z.-K. Mohammad, Modelling long-term groundwater fluctuations by extreme learning machine using hydro-climatic data, Hydrol. Sci. J., 63 (2018) 63–73.
16. S.I. Abba, A.G. Usman, Y.A. Danmaraya, A.G. Usman, H.U. Abdullahi, Modeling of water treatment plant performance using artificial neural network: case study Tamburawa Kano-Nigeria, DUJOPAS, 6 (2020) 135–144.
17. B. Maryam, H. Büyükgüngör, Wastewater reclamation and reuse trends in Turkey: opportunities and challenges, J. Water Process Eng., 30 (2019) 100501, doi: 10.1016/j.jwpe. 2017.10.001.
18. V. Nourani, G. Elkiran, S.I. Abba, Wastewater treatment plant performance analysis using artificial intelligence – an ensemble approach, Water Sci. Technol., 78 (2018) 477 (1–14), doi: 10.2166/ wst.2018.477.
19. K.B. Newhart, R.W. Holloway, A.S. Hering, T.Y. Cath, Datadriven performance analyses of wastewater treatment plants: a review, Water Res., 157 (2019) 498–513.
20. S.I. Abba, Q.B. Pham, A.G. Usman, N.T.T. 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, doi: 10.1016/j.jwpe.2019.101081.
21. S.I. Abba, S.J. Hadi, J. Abdullahi, River water modelling prediction using multi-linear regression, artificial neural network, and adaptive neuro-fuzzy inference system techniques, Procedia Comput. Sci., 120 (2017) 75–82.
22. R. Costache, Q.B. Pham, E. Sharifi, N.T.T. Linh, S.I. Abba, M. Vojtek, J. Vojteková, P.T.T. Nhi, D.N. Khoi, Flash-flood susceptibility assessment using multi-criteria decision making and machine learning supported by remote sensing and GIS techniques, Remote Sens., 12 (2020) 106, doi: 10.3390/ RS12010106.
23. S.J. Hadi, S.I. Abba, S.S. Sammen, S.Q. Salih, A.-A. Nadhir, Z.M. Yaseen, Nonlinear input variable selection approach integrated with non-tuned data intelligence model for streamflow pattern simulation, IEEE Access, 7 (2019) 141533– 141548.
24. A.K. Verma, T.N. Singh, Prediction of water quality from simple field parameters, Environ. Earth Sci., 69 (2013) 821–829.
25. F. Granata, S. Papirio, G. Esposito, R. Gargano, G. de Marinis, Machine learning algorithms for the forecasting of wastewater quality indicators, Water, 9 (2017) 1–12.
26. M.S. Gaya, M.U. Zango, L.A. Yusuf, M. Mustapha, B. Muhammad, A. Sani, A. Tijjani, N.A. Wahab, M.T.M. 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.
27. UNDP, New Nicosia Waste Water Treatment Plant, United Nations Development Programme, Nicosia, Northern Part of Cyprus, 2014.
28. S.I. Abba, G. Elkiran, Effluent prediction of chemical oxygen demand from the astewater treatment plant using artificial neural network application, Procedia Comput. Sci., 120 (2017) 156–163.
29. Z.-Y. Chen, T.-H. Zhang, R. Zhang, Z.-M. Zhu, J. Yung, P.-Y. Chen, C.-Q. Ou, Y. Guo, Extreme gradient boosting model to estimate PM2.5 concentrations with missing-filled satellite data in China, Atmos. Environ., 202 (2019) 180–189.
30. K. Zarei, M. Atabati, M. Ahmadi, Shuffling cross–validation– bee algorithm as a new descriptor selection method for retention studies of pesticides in biopartitioning micellar chromatography, J. Environ. Sci. Health., Part B, 52 (2017) 346–352.
31. R.G. Sargent, Verification and Validation of Simulation Models, Proceedings of the 2010 Winter Simulation Conference, IEEE, Baltimore, MD, USA, 2010, pp. 278–289.
32. M. Valipour, M.E. Banihabib, S.M.R. Behbahani, Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez Dam Reservoir, J. Hydrol., 476 (2013) 433–441.
33. S.I. Abba, M.S. Gaya, M.L. Yakubu, M.U. Zango, R.A. Abdulkadir, M.A. Saleh, A.N. Hamza, U. Abubakar, A.I. Tukur, N.A. Wahab, Modelling of Uncertain System: A Comparison Study of Linear and Nonlinear Approaches, 2019 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS), IEEE, Selangor, Malaysia, Malaysia, 2019.
34. S.I. Abba, N.T.T. Linh, J. Abdullahi, S.I.A. Ali, Q.B. Pham, R.A. Abdulkadir, R. Costache, V.T. Nam, D.T. Anh, Hybrid machine learning ensemble techniques for modeling dissolved oxygen concentration, IEEE Access, 8 (2020) 157218–157237, doi: 10.1109/ACCESS.2020.3017743.
35. 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.
36. 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 modelling of rainfall, Water Resour. Manage., 33 (2019) 5067–5087.
37. V. Nourani, G. Elkiran, S.I. Abba, Multi-parametric modeling of water treatment plant using AI-based nonlinear ensemble, J. Water Supply Res. Technol. AQUA, 2 (2019) 1–15.
38. A.A. Godarzi, R.M. Amiri, A. Talaei, T. Jamasb, Predicting oil price movements: a dynamic artificial neural network approach, Energy Policy, 68 (2014) 371–382.
39. L.S. Gomes, F.A.A. Souza, R.S.T. Pontes, T.R.F. Neto, R.A.M. Araújo, Coagulant dosage determination in a water treatment plant using dynamic neural network models, Int. J. Comput. Intell. Appl., 14 (2015) 1–18.
40. F. Fahimi, Z.M. Yaseen, A. El-shafie, Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review, Theor. Appl. Climatol., 128 (2016) 1–29.
41. Z.M. Yaseen, M.F. Allawi, A.A. Yousif, O. Jaafar, F.M. Hamzah, A. El-Shafie, Non-tuned machine learning approach for hydrological time series forecasting, Neural Comput. Appl.,30 (2018) 1479–1491.
42. R. Eisinga, M. Te Grotenhuis, B. Pelzer, The reliability of a two-item scale: Pearson, Cronbach, or Spearman-brown?, Int. J. Public Health, 58 (2013) 637–642.
43. S.I. Abba, A.G. Usman, S. Isik, Simulation for response surface in the HPLC optimization method development using artificial intelligence models: a data-driven approach, Lab. Syst., 201 (2020) 104007, doi: 10.1016/j.chemolab.2020.104007.
44. K.P. Singh, A. Basant, A. Malik, G. Jain, Artificial neural network modeling of the river water quality — a case study, 220 (2009) 888–895.
45. G.E.P. Box, G.M. Jenkins, G.C. Reinsel, G.M. Ljung, Time Series Analysis: Forecasting and Control, John Wiley & Sons, 2015.
46. K.E. Taylor, Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res., 106 (2001) 7183–7192.
47. S. Zhu, S. Heddam, E.K. Nyarko, M. Hadzima-Nyarko, S. Piccolroaz, S. 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.
48. A. Najah, A. El-Shafie, O.A. Karim, A.H. El-Shafie, Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring, Environ. Sci. Pollut. Res., 21 (2014) 1658–1670.