Abstract
Monitoring water quality is of great importance and mainly adopted for water pollution control of conventional and nonconventional water resources. Generally, water quality is evaluated using several indicators, including chemical oxygen demand (COD), biochemical oxygen demand (BOD), and dissolved oxygen concentration (DO). In the present investigation, two artificial intelligence techniques, namely, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANN), were applied for predicting two water quality indicators: (1) chemical oxygen demand (COD) at Sidi Marouane Wastewater Treatment Plant (WWTP), east of Algeria, and (2) dissolved oxygen concentration (DO) at the drinking water treatment plant of Boudouaou, Algeria. The models were developed and compared based on several water quality variables as inputs. Three ANFIS models, namely, (1) ANFIS with fuzzy c-mean clustering (FCM) algorithm called ANFIS_FC, (2) ANFIS with grid partition (GP) method called ANFIS_GP, and (3) ANFIS with subtractive clustering (SC) called ANFIS_SC, were developed. The ANFIS models were compared to standard multilayer perceptron neural network (MLPNN) and multiple linear regression model (MLR). Results obtained demonstrated that (1) for predicting COD, ANFIS_SC is the best model, and the coefficient of correlation (R), Wilmot’s index (d), root-mean-square error (RMSE), and mean absolute error (MAE) were calculated as 0.805, 0.880, 6.742, and 4.944 mg/L for the validation dataset. The worst results were obtained using the MLR model with R, d, RMSE, and MAE equal to 0.750, 0.840, 0.7658, and 5.916 mg/L for the validation subset, and (2) for predicting DO concentration, the best results were obtained using ANFIS_SC with R, d, RMSE, and MAE equal to 0.856, 0.922, 1.528, and 1.123 mg/L for the validation subset, respectively.
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References
Kisi O, Ay M (2014) Comparison of Mann-Kendall and innovative trend method for water quality parameters of the Kizilirmak River, Turkey. J Hydrol 513:362–375. https://doi.org/10.1016/j.jhydrol.2014.03.005
Cong Q, Yu W (2018) Integrated soft sensor with wavelet neural network and adaptive weighted fusion for water quality estimation in wastewater treatment process. Measurement 124:436–446. https://doi.org/10.1016/j.measurement.2018.01.001
Xiao H, Huang D, Pan Y, Liu Y, Song K (2017) Fault diagnosis and prognosis of wastewater processes with incomplete data by the auto-associative neural networks and ARMA model. Chemom Intell Lab Syst 161:96–107
Ruan J, Zhang C, Li Y, Li P, Yang Z, Chen X, Huang M, Zhang T (2017) Improving the efficiency of dissolved oxygen control using an on-line control system based on a genetic algorithm evolving FWNN software sensor. J Environ Manag 187:550–559. https://doi.org/10.1016/j.jenvman.2016.10.056
Fernandez de Canete J, Del Saz Orozco P, Baratti R, Mulas M, Ruano A, Garcia-Cerezo A (2016) Soft-sensing estimation of plant effluent concentrations in a biological wastewater treatment plant using an optimal neural network. Expert Syst Appl 63(8):19. https://doi.org/10.1016/j.eswa.2016.06.028
Ay M, Kisi O (2014) Modelling of chemical oxygen demand by using ANNs, ANFIS and k-means clustering techniques. J Hydrol 511:279–289
Kisi O, Parmar KS (2016) Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution. J Hydrol 534:104–112
Nadiri AA, Shokri S, Tsai FTC, Moghaddam AA (2018) Prediction of effluent quality parameters of a wastewater treatment plant using a supervised committee fuzzy logic model. J Clean Prod 180:539–549. https://doi.org/10.1016/j.jclepro.2018.01.139
Moral H, Aksoy A, Gokcay CF (2008) Modeling of the activated sludge process by using artificial neural networks with automated architecture screening. Comput Chem Eng 32:2471–2478. https://doi.org/10.1016/j.compchemeng.2008.01.008
Yilmaz T, Seckin G, Yuceer A (2010) Modeling of effluent COD in UAF reactor treating cyanide containing wastewater using artificial neural network approaches. Adv Eng Softw 41:1005–1010. https://doi.org/10.1016/j.advengsoft.2010.04.002
Pai TY, Yang PY, Wang SC, Lo MH, Chiang CF, Kuo JL, Chu HH, Su HC, Yu LF, Hu HC, Chang YH (2011) Predicting effluent from the wastewater treatment plant of industrial park based on fuzzy network and influent quality. Appl Math Model 35:3674–3684. https://doi.org/10.1016/j.apm.2011.01.019
Perendeci A, Arslan S, Tanyolaç A, Celebi SS (2009) Effects of phase vector and history extension on prediction power of adaptive-network based fuzzy inference system (ANFIS) model for a real scale anaerobic wastewater treatment plant operating under unsteady state. Bioresour Technol 100:4579–4587
Singh KP, Basant N, Malik A, Jain G (2010) Modeling the performance of “up-flow anaerobic sludge blanket” reactor based wastewater treatment plant using linear and nonlinear approaches-a case study. Anal Chim Acta 658:1–11
Yang T, Zhang L, Wang A, Gao H (2013) Fuzzy modeling approach to predictions of chemical oxygen demand in activated sludge processes. Inf Sci 235:55–64
Erdirencelebi D, Yalpir S (2011) Adaptive network fuzzy inference system modeling for the input selection and prediction of anaerobic digestion effluent quality. Appl Math Model 35:3821–3832. https://doi.org/10.1016/j.apm.2011.02.015
Heddam S, Lamda H, Filali S (2016) Predicting effluent biochemical oxygen demand in a wastewater treatment plant using generalized regression neural network based approach: a comparative study. Environ Process 3:153–165
Heddam S, Bermad A, Dechemi N (2011) Applications of radial basis function and generalized regression neural networks for modelling of coagulant dosage in a drinking water treatment: a comparative study. J Environ Eng 137(12):1209–1214
Heddam S, Bermad A, Dechemi N (2012) ANFIS-based modelling for coagulant dosage in drinking water treatment plant: a case study. Environ Monit Assess 184:1953–1971. https://doi.org/10.1007/s10661-011-2091-x
Heddam S, Dechemi N (2015) A new approach based on the dynamic evolving neural-fuzzy inference system (DENFIS) for modelling coagulant dosage: case study of water treatment plant of Algeria country. Desalin Water Treat 53(4):1045–1053. https://doi.org/10.1080/19443994.2013.878669
Olden JD, Jackson DA (2002) Illuminating the “black box”: understanding variable contributions in artificial neural networks. Ecol Model 154:135–150
Houichi L, Dechemi N, Heddam S, Achour B (2013) An evaluation of ANN methods for estimating the lengths of hydraulic jumps in U-shaped channel. J Hydroinf 15(1):147–154
Heddam S (2014) Modelling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): a comparative study. Environ Monit Assess 186:597–619. https://doi.org/10.1007/s10661-013-3402-1
Keshtegar B, Heddam S (2018) Modeling daily dissolved oxygen concentration using modified response surface method and artificial neural network: a comparative study. Neural Comput Appl 30(10):2995–3006. https://doi.org/10.1007/s00521-017-2917-8
Haykin S (1999) Neural networks a comprehensive foundation. Prentice Hall, Upper Saddle River
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart DE, McClelland PDP, Research Group (eds) Parallel distributed processing: explorations in the microstructure of cognition. Foundations, vol I. MIT Press, Cambridge, pp 318–362
Hornik K (1991) Approximation capabilities of multilayer feedforward networks. Neural Netw 4(2):251–257. https://doi.org/10.1016/0893-6080(91)90009-T
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal Approximators. Neural Netw 2:359–366. https://doi.org/10.1016/0893-6080(89)90020-8
Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685
Zhu S, Heddam S, Nyarko E, Hadzima-Nyarko M, Piccolroaz S, Wu S (2019) Modelling daily water temperature for rivers: adaptive neuro-fuzzy inference systems vs. artificial neural networks models. Environ Sci Pollut Res 26(1):402–420. https://doi.org/10.1007/s11356-018-3650-2
Zhu S, Heddam S, Wu S, Dai J, Jia B (2019) Extreme learning machine based prediction of daily water temperature for rivers. Environ Earth Sci 78:202
Zhu S, Nyarko E, Hadzima-Nyarko M, Heddam S, Wu S (2019) Assessing the performance of a suite of machine learning models for daily river water temperature prediction. PeerJ 7:e7065
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Heddam, S., Kisi, O., Sebbar, A., Houichi, L., Djemili, L. (2019). Predicting Water Quality Indicators from Conventional and Nonconventional Water Resources in Algeria Country: Adaptive Neuro-Fuzzy Inference Systems Versus Artificial Neural Networks. In: Negm, A.M., Bouderbala, A., Chenchouni, H., Barceló, D. (eds) Water Resources in Algeria - Part II. The Handbook of Environmental Chemistry, vol 98. Springer, Cham. https://doi.org/10.1007/698_2019_399
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DOI: https://doi.org/10.1007/698_2019_399
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