References
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- M. Negnevitsky, Artificial Intelligence A Guide to Intelligent
Systems, 2nd ed., Vol. 123, Pearson Education, England, 2005.
- S. Akkurt, G. Tayfur, S. Can, Fuzzy logic model for the
prediction of cement compressive strength, Cem. Concr. Res.,
34 (2004) 1429–1433.
- 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.
- 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.
- 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.
- V. Vaidhehi, The role of dataset in training ANFIS system for
course advisor, Int. J. Innov. Res. Adv. Eng., 1 (2014) 2349–2163.
- 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.
- J.-S.R. Jang, ANFIS: adaptive-network-based fuzzy inference
system, IEEE Trans. Syst. Man Cybern., 23 (1993) 665–685.
- MATLAB, The MathWorks Inc. Version R2022b, The MathWorks
Inc., The MathWorks, Inc., United States, 2022. Available at:
https://matlab.mathworks.com
- Y.-M. Wang, T.M.S. Elhag, An adaptive neuro-fuzzy inference
system for bridge risk assessment, Expert Syst. Appl., 34 (2008)
3099–3106.
- 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.
- 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.