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

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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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Authors and 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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