Investigating the management performance of disinfection analysis of water distribution networks using data mining approaches

  • Mohammad Zounemat-Kermani
  • Abdollah Ramezani-Charmahineh
  • Jan Adamowski
  • Ozgur Kisi


Chlorination, the basic treatment utilized for drinking water sources, is widely used for water disinfection and pathogen elimination in water distribution networks. Thereafter, the proper prediction of chlorine consumption is of great importance in water distribution network performance. In this respect, data mining techniques—which have the ability to discover the relationship between dependent variable(s) and independent variables—can be considered as alternative approaches in comparison to conventional methods (e.g., numerical methods). This study examines the applicability of three key methods, based on the data mining approach, for predicting chlorine levels in four water distribution networks. ANNs (artificial neural networks, including the multi-layer perceptron neural network, MLPNN, and radial basis function neural network, RBFNN), SVM (support vector machine), and CART (classification and regression tree) methods were used to estimate the concentration of residual chlorine in distribution networks for three villages in Kerman Province, Iran. Produced water (flow), chlorine consumption, and residual chlorine were collected daily for 3 years. An assessment of the studied models using several statistical criteria (NSC, RMSE, R2, and SEP) indicated that, in general, MLPNN has the greatest capability for predicting chlorine levels followed by CART, SVM, and RBF-ANN. Weaker performance of the data-driven methods in the water distribution networks, in some cases, could be attributed to improper chlorination management rather than the methods’ capability.


Residual chlorine Artificial neural network (ANN) Water treatment Decision tree (DT) Radial basis function (RBF) Disinfection 


  1. Aghaarabi, E., Aminravan, F., Sadiq, R., Hoorfar, M., Rodriguez, M. J., & Najjaran, H. (2014). Comparative study of fuzzy evidential reasoning and fuzzy rule-based approaches: An illustration for water quality assessment in distribution networks. Stochastic Environmental Research and Risk Assessment, 28(3), 655–679. Scholar
  2. Akbarizadeh, M., Daghbandan, A., & Yaghoobi, M. (2013). Modeling and optimization of poly electrolyte dosage in water treatment process by GMDH type- NN and MOGA. International Journal of Chemoinformatics and Chemical Engineering (IJCCE), 3(2), 94–106. Scholar
  3. Ammar, T. A., Abid, K. Y., El-Bindary, A. A., & El-Sonbati, A. Z. (2014). Chlorine dioxide bulk decay prediction in desalinated drinking water. Desalination, 352, 45–51. Scholar
  4. Andrade, M. A., Choi, C. Y., Lansey, K., & Jung, D. (2016). Enhanced artificial neural networks estimating water quality constraints for the optimal water distribution systems design. Journal of Water Resources Planning and Management, 142(9), 04016024–1–04016024–14. Scholar
  5. Bowden, G. J., Nixon, J. B., Dandy, G. C., Maier, H. R., & Holmes, M. (2006). Forecasting chlorine residuals in a water distribution system using a general regression neural network. Mathematical and Computer Modelling, 44(5–6), 469–484. Scholar
  6. Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A., 1984. Classification and regression trees. CRC press, Taylor & Francis, USA.Google Scholar
  7. Cervantes, D. H., Rodríguez, J. M., Galván, X. D., Medel, J. O., & Magaña, M. R. J. (2016). Optimal use of chlorine in water distribution networks based on specific locations of booster chlorination: Analyzing conditions in Mexico. Water Science and Technology: Water Supply, 16(2), 493–505. Scholar
  8. Chang, K., Gao, J. L., Wu, W. Y., & Yuan, Y. X. (2011). Water quality comprehensive evaluation method for large water distribution network based on clustering analysis. Journal of Hydroinformatics, 13(3), 390–400. Scholar
  9. Dibike, Y. B., Velickov, S., Solomatine, D., & Abbott, M. B. (2001). Model induction with support vector machines: Introduction and applications. Journal of Computing in Civil Engineering, 15(3), 208–216. CrossRefGoogle Scholar
  10. EPA (United States Environmental Protection Agency) (2006). Drinking water standards and health advisory tables. EPA 822-R-06-013, Washington DC.Google Scholar
  11. Gibbs, M. S., Morgan, N., Maier, H. R., Dandy, G. C., Nixon, J. B., & Holmes, M. (2006). Investigation into the relationship between chlorine decay and water distribution parameters using data driven methods. Mathematical and Computer Modelling, 44(5–6), 485–498. Scholar
  12. Karadirek, I. E., Soyupak, S., & Muhammetoglu, H. (2016). Chlorine modeling in water distribution networks using ARX and ARMAX model structures. Desalination and Water Treatment, 57(25), 11592–11598. Scholar
  13. Loh, W. Y. (2011). Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(1), 14–23. Scholar
  14. May, R. J., Dandy, G. C., Maier, H. R., & Nixon, J. B. (2008). Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems. Environmental Modelling & Software, 23(10–11), 1289–1299. Scholar
  15. Nejjari, F., Puig, V., Perez, R., Quevedo, J., Cuguero, M. A., Sanz, G., & Mirats, J. M. (2014). Chlorine decay model calibration and comparison: Application to a real water network. Procedia Engineering, 70, 1221–1230. Scholar
  16. Rodriguez, M. J., & Sérodes, J. B. (1998). Assessing empirical linear and non-linear modelling of residual chlorine in urban drinking water systems. Environmental Modelling & Software, 14(1), 93–102. Scholar
  17. Sentas, A., Psilovikos, A., Psilovikos, T., & Matzafleri, N. (2016). Comparison of the performance of stochastic models in forecasting daily dissolved oxygen data in dam-Lake thesaurus. Desalination and Water Treatment, 57(25), 11660–11674. Scholar
  18. Sharif, M. N., Farahat, A., Haider, H., Al-Zahrani, M. A., Rodriguez, M. J., & Sadiq, R. (2017). Risk-based framework for optimizing residual chlorine in large water distribution systems. Environmental Monitoring and Assessment, 189(7), 307, 1–19. Scholar
  19. Smola, A. J., & Scholkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14(3), 199–222.CrossRefGoogle Scholar
  20. Soyupak, S., Kilic, H., Karadirek, I. E., & Muhammetoglu, H. (2011). On the usage of artificial neural networks in chlorine control applications for water distribution networks with high quality water. Journal of Water Supply: Research and Technology-AQUA, 60(1), 51–60. Scholar
  21. Timofeev, R., (2004). Classification and regression trees (cart) theory and applications. Master Thesis, Humboldt University, Berlin.Google Scholar
  22. Venkatesh Prabhu, M., Karthikeyan, R., & Shanmugaprakash, M. (2016). Modeling and optimization by response surface methodology and neural network–genetic algorithm for decolorization of real textile dye effluent using Pleurotus ostreatus: A comparison study. Desalination and Water Treatment, 57(28), 13005–13019. Scholar
  23. Wu, W., Dandy, G. C., & Maier, H. R. (2015). Optimal control of total chlorine and free ammonia levels in a water transmission pipeline using artificial neural networks and genetic algorithms. Journal of Water Resources Planning and Management, 141(7), 123–135. CrossRefGoogle Scholar
  24. Zounemat-Kermani, M., Kisi, Ö., Adamowski, J., & Ramezani-Charmahineh, A. (2016). Evaluation of data driven models for river suspended sediment concentration modeling. Journal of Hydrology, 535, 457–472. Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of water engineeringShahid Bahonar University of KermanKermanIran
  2. 2.Department of Bioresource Engineering, Faculty of Agriculture and Environmental SciencesMcGill UniversityQuebecCanada
  3. 3.Faculty of Natural Sciences and EngineeringIlia State UniversityTbilisiGeorgia

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