Heddam and Dechem (2015)Heddam, S., and Dechem, N., A new approach based on the dynamic evolving neural-fuzzy inference system (DENFIS) for modeling coagulat dosage (Dos): Case study of water treatment plant of Algeria. Desalination and Water Treatment, 53(4), 1045-1053 (2005).
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Neural-fuzzy inference system (DENFIS) |
Seven online measurements of raw water and alum dosage |
Heddan et al (2011)Heddam, S., Abdelmalek, B., and Dechemi, N., Applications of radial-basis function and generalized regression neural networks for modeling of coagulant dosage in drinking water-treatment plant: Comparative study. Journal of Environmental Engineering, 137(12), 1209-1214 (2001).
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Generalized regression neural network (GRNN) and radial basis neural network (RBFNN) |
Six input variables 725 samples in 2 year-period |
Heddam et al. (2012)Heddam, S., Bernard, A., and Dechemi, N., ANFIS-based modelling for coagulant dosage in drinking water treatment plant: a case study. Environmental Monitoring and Assessment, 184(4), 1953-1971 (2012).
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Adaptative neuro-fuzzy inference system (ANFIS) |
Seven online measurements of raw water and alum dosage |
Hernandez and Le Lann (2006)Hernandez, H., and Le Lann, M.-V., Development of a neural sensor for on-line prediction of coagulant dosage in a potable water treatment plant in the way of its diagnosis. J. S. Sichman et al (Eds.) IBERAMIA-SBIA, LNAI 4140, 249-257. Springer-Verlag Berlin Keideberg(2006).
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ANN and linear regression model |
9 water quality variables |
Lamrini et al. (2005)Lamrini, B., Benhammou, A., Le Lann, M.-V. and Karama, A., A neural software sensor for online prediction of coagulant dosage in a drinking water treatment plant. Transactions of the Institute of Measurement and Control, 27(3), 195-213 (2005).
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Neural-fuzzy inference system |
Seven online measurements of raw water and alum dosage. |
Robenson (2009)Robenson, A., Shukor, S.A., and Aziz, N., Development of process inverse neural network model to determine the required alum dosage at Segama water treatment plant Sabah, Malaysia. Computer Aided Chemical Engineering, 27, 525-530 (2009).
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Inverse neural network |
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Wu and Lo (2008)Wu, G.-D., and Lo, S.-L., Predicting real-time coagulant dosage in water treatment by artificial neural networks and adaptive network-based fuzzy inference system. Engineering Applications of Artificial Intelligence, 21(8), 1189-1195 (2008).
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Levenberg-Marquardt neural networks |
Raw water turbidity and coagulant dosage on day t − 1 |
Zhang et al. (2013)Zhang, K., Achari, G., Li, H., Zargar, A., and Sadiq, R., Machine learning approaches to predict coagulant dosage in water treatment plants. Int. J. Assur. Eng. Manag., 4(2), 205-214 (2013).
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K-nearest neighbors (KNN) |
3 water quality variables 966 samples collected between 2008 and 2010 |