Articles | Volume 6, issue 1
https://doi.org/10.5194/dwes-6-39-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.Predicting the residual aluminum level in water treatment process
Related authors
Related subject area
Tools: Modeling and simulation
Algorithms for optimization of branching gravity-driven water networks
The effect of a loss of model structural detail due to network skeletonization on contamination warning system design: case studies
Limitations of demand- and pressure-driven modeling for large deficient networks
Identifying (subsurface) anthropogenic heat sources that influence temperature in the drinking water distribution system
Drink. Water Eng. Sci., 11, 67–85,
2018Drink. Water Eng. Sci., 11, 49–65,
2018Drink. Water Eng. Sci., 10, 93–98,
2017Drink. Water Eng. Sci., 10, 83–91,
2017Cited articles
Areerachakul, S. and Sanguansintukul, S.: A Comparison between the Multiple Linear Regression Model and Neural Networks for Biochemical Oxygen Demand Estimations, SNLP '09, Eighth International Symposium on Natural Language Processing, 11–14, 2009.
Audone, B. and Giunta, G.: Multiple Linear Regression to Detect Shielding Effectiveness Degradations. International Symposium on Electromagnetic Compatibility – EMC EUROPE 2008, 8–12 September 2008, 1–6, 2008.
Baxter, C. W., Stanley, S. J., and Zhang, Q.: Development of a full-scale artificial neural network model for the removal of natural organic matter by enhanced coagulation, J. Water Supply Res. T., 48, 129–136, 1999.
Baxter, C. W., Zhang, Q., Stanley, S. J., Shariff, R., Tupas, R.-R. T., and Stark, H. L.: Drinking water quality and treatment: the use of artificial neural networks, Can. J. Civil Eng., 28 (Suppl. 1), 26–35, 2001.
Beale, M. H., Hagan, M. T., and Demuth, H. B.: Neural Network Toolbox TM 7 User's Guide, Matlab MathWorks Inc., September 2010.