1. H.Y.H. Alnajjar, O. Üçüncü, Using of a fuzzy logic as one of the artificial intelligence models to increase the efficiency of the biological treatment ponds in wastewater treatment plants, Int. J. Environ. Pollut. Environ. Modell., 4 (2021) 85–94.
  2. T.Y. Pai, T.J. Wan, S.T. Hsu, T.C. Chang, Y.P. Tsai, C.Y. Lin, H.C. Su, L.F. Yu, Using fuzzy inference system to improve neural network for predicting hospital wastewater treatment plant effluent, Comput. Chem. Eng., 33 (2009) 1272–1278.
  3. M.S. Gaya, N.A. Wahab, Y.M. Sam, S.I. Samsuddin, ANFISbased effluent pH quality prediction model for an activated sludge process, Adv. Mater. Res., 845 (2014) 538–542.
  4. K. Yetilmezsoy, H. Ozgun, R.K. Dereli, M.E. Ersahin, I. Ozturk, Adaptive neuro-fuzzy inference-based modeling of a full-scale expanded granular sludge bed reactor treating corn processing wastewater, J. Intell. Fuzzy Syst., 28 (2015) 1601–1616.
  5. V. Nourani, P. Asghari, E. Sharghi, Artificial intelligence based ensemble modeling of wastewater treatment plant using jittered data, J. Cleaner Prod., 291 (2021) 125772, doi: 10.1016/j.jclepro.2020.125772.
  6. D.O. Araromi, O.T. Majekodunmi, J.A. Adeniran, T.O. Salawudeen, Modeling of an activated sludge process for effluent prediction—a comparative study using ANFIS and GLM regression, Environ. Monit. Assess., 190 (2018) 495, doi: 10.1007/s10661-018-6878-x.
  7. D.S. Manu, A.K. Thalla, Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl nitrogen from wastewater, Appl. Water Sci., 7 (2017) 3783–3791.
  8. E. Hong, A.M. Yeneneh, T.K. Sen, H.M. Ang, A. Kayaalp, ANFIS based modelling of dewatering performance and polymer dose optimization in a wastewater treatment plant, J. Environ. Chem. Eng., 6 (2018) 1957–1968.
  9. M.S. Gaya, N.A. Wahab, Y.M. Sam, A.N. Anuar, S.I. Samsuddin, ANFIS modelling of carbon removal in domestic wastewater treatment plant, Appl. Mech. Mater., 372 (2013) 597–601.
  10. M. Negnevitsky, Artificial Intelligence A Guide to Intelligent Systems, 2nd ed., Vol. 123, Pearson Education, England, 2005.
  11. S. Akkurt, G. Tayfur, S. Can, Fuzzy logic model for the prediction of cement compressive strength, Cem. Concr. Res., 34 (2004) 1429–1433.
  12. F.I. Turkdogan-Aydinol, K. Yetilmezsoy, A fuzzy-logic-based model to predict biogas and methane production rates in a pilotscale mesophilic UASB reactor treating molasses wastewater, J. Hazard. Mater., 182 (2010) 460–471.
  13. D. Erdirencelebi, S. Yalpir, Adaptive network fuzzy inference system modeling for the input selection and prediction of anaerobic digestion effluent quality, Appl. Math. Modell., 35 (2011) 3821–3832.
  14. Z. Hu, Y.V. Bodyanskiy, O.K. Tyshchenko, Self-Learning and Adaptive Algorithms for Business Applications: A Guide to Adaptive Neuro-fuzzy Systems for Fuzzy Clustering under Uncertainty Conditions, No. 2019, Emerald Publishing Limited, United Kingdom, 2019.
  15. V. Vaidhehi, The role of dataset in training ANFIS system for course advisor, Int. J. Innov. Res. Adv. Eng., 1 (2014) 2349–2163.
  16. T. Takagi, M. Sugeno, Fuzzy identification of systems and its applications to modeling and control, IEEE Trans. Syst. Man Cybern., SMC-15 (1985) 116–132.
  17. J.-S.R. Jang, ANFIS: adaptive-network-based fuzzy inference system, IEEE Trans. Syst. Man Cybern., 23 (1993) 665–685.
  18. MATLAB, The MathWorks Inc. Version R2022b, The MathWorks Inc., The MathWorks, Inc., United States, 2022. Available at:
  19. Y.-M. Wang, T.M.S. Elhag, An adaptive neuro-fuzzy inference system for bridge risk assessment, Expert Syst. Appl., 34 (2008) 3099–3106.
  20. J. Wan, M. Huang, Y. Ma, W. Guo, Y. Wang, H. Zhang, W. Li, X. Sun, Prediction of effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy inference system, Appl. Soft Comput., 11 (2011) 3238–3246.
  21. Z. Cheng, X. Li, Y. Bai, C. Li, Multi-scale fuzzy inference system for influent characteristic prediction of wastewater treatment, Clean - Soil, Air, Water, 46 (2018) 1–11.