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

Environmental Monitoring and Assessment volume 190, Article number: 397 (2018) Cite this article

Abstract

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.

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References

  • 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. https://doi.org/10.1007/s00477-013-0780-4.

    Article  Google Scholar 

  • 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. https://doi.org/10.4018/ijcce.2013070107.

    Article  CAS  Google Scholar 

  • 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. https://doi.org/10.1016/j.desal.2014.08.010.

    Article  CAS  Google Scholar 

  • 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. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000663.

    Article  Google Scholar 

  • 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. https://doi.org/10.1016/j.mcm.2006.01.006.

    Article  Google Scholar 

  • Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A., 1984. Classification and regression trees. CRC press, Taylor & Francis, USA.

  • 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. https://doi.org/10.2166/ws.2015.161.

    CAS  Article  Google Scholar 

  • 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. https://doi.org/10.2166/hydro.2011.021.

    Article  Google Scholar 

  • 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. https://doi.org/10.1061/(ASCE)0887-3801(2001)15:3(208).

    Article  Google Scholar 

  • EPA (United States Environmental Protection Agency) (2006). Drinking water standards and health advisory tables. EPA 822-R-06-013, Washington DC.

  • 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. https://doi.org/10.1016/j.mcm.2006.01.007.

    Article  Google Scholar 

  • 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. https://doi.org/10.1080/19443994.2015.1065769.

    Article  CAS  Google Scholar 

  • Loh, W. Y. (2011). Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(1), 14–23. https://doi.org/10.1002/widm.8.

    Article  Google Scholar 

  • 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. https://doi.org/10.1016/j.envsoft.2008.03.008.

    Article  Google Scholar 

  • 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. https://doi.org/10.1016/j.proeng.2014.02.135.

    Article  Google Scholar 

  • 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. https://doi.org/10.1016/S1364-8152(98)00061-9.

    Article  Google Scholar 

  • 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. https://doi.org/10.1080/19443994.2015.1128984.

    Article  Google Scholar 

  • 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. https://doi.org/10.1007/s10661-017-5989-0.

    Article  CAS  Google Scholar 

  • Smola, A. J., & Scholkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14(3), 199–222.

    Article  Google Scholar 

  • 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. https://doi.org/10.2166/aqua.2011.086.

    Article  CAS  Google Scholar 

  • Timofeev, R., (2004). Classification and regression trees (cart) theory and applications. Master Thesis, Humboldt University, Berlin.

  • 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. https://doi.org/10.1080/19443994.2015.1059372.

    Article  CAS  Google Scholar 

  • 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. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000486.

    Article  Google Scholar 

  • 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. https://doi.org/10.1016/j.jhydrol.2016.02.012.

    Article  Google Scholar 

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Author information

Affiliations

  1. Department of water engineering, Shahid Bahonar University of Kerman, Kerman, Iran

    Mohammad Zounemat-Kermani & Abdollah Ramezani-Charmahineh

  2. Department of Bioresource Engineering, Faculty of Agriculture and Environmental Sciences, McGill University, Sainte-Anne-de-Bellevue, Quebec, Canada

    Jan Adamowski

  3. Faculty of Natural Sciences and Engineering, Ilia State University, Tbilisi, Georgia

    Ozgur Kisi

Authors
  1. Mohammad Zounemat-Kermani
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  2. Abdollah Ramezani-Charmahineh
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  3. Jan Adamowski
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  4. Ozgur Kisi
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Corresponding author

Correspondence to Mohammad Zounemat-Kermani.

Additional information

• This study examines the applicability of three data-driven approaches for predicting chlorine levels in four water distribution networks

• The data included produced water flow (Q), daily chlorine consumption (C), and daily residual chlorine (RC) in m3/day, kg/L, and mg/L

• MLPNN has the greatest capability for predicting chlorine levels followed by CART, SVM, and RBF-ANN

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Zounemat-Kermani, M., Ramezani-Charmahineh, A., Adamowski, J. et al. Investigating the management performance of disinfection analysis of water distribution networks using data mining approaches. Environ Monit Assess 190, 397 (2018). https://doi.org/10.1007/s10661-018-6769-1

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  • DOI: https://doi.org/10.1007/s10661-018-6769-1

Keywords

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