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A hybrid regression model for water quality prediction

Author

Listed:
  • Tanujit Chakraborty

    (Indian Statistical Institute)

  • Ashis Kumar Chakraborty

    (Indian Statistical Institute)

  • Zubia Mansoor

    (Amity University)

Abstract

In this work, we propose a hybrid regression model to solve a specific problem faced by a modern paper manufacturing company. Boiler inlet water quality is a major concern for the paper machine. If water treatment plant can not produce water of desired quality, then it results in poor health of the boiler water tube and consequently affects the quality of the paper. This variation is due to several crucial process parameters. We build a hybrid regression model based on regression tree and support vector regression for boiler water quality prediction and show its excellent performance as compared to other state-of-the-art.

Suggested Citation

  • Tanujit Chakraborty & Ashis Kumar Chakraborty & Zubia Mansoor, 2019. "A hybrid regression model for water quality prediction," OPSEARCH, Springer;Operational Research Society of India, vol. 56(4), pages 1167-1178, December.
    • Handle: RePEc:spr:opsear:v:56:y:2019:i:4:d:10.1007_s12597-019-00386-z
    DOI: 10.1007/s12597-019-00386-z
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    References listed on IDEAS

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    1. Singh, Kunwar P. & Basant, Ankita & Malik, Amrita & Jain, Gunja, 2009. "Artificial neural network modeling of the river water quality—A case study," Ecological Modelling, Elsevier, vol. 220(6), pages 888-895.
    2. Tanujit Chakraborty & Swarup Chattopadhyay & Ashis Kumar Chakraborty, 2018. "A novel hybridization of classification trees and artificial neural networks for selection of students in a business school," OPSEARCH, Springer;Operational Research Society of India, vol. 55(2), pages 434-446, June.
    3. Chakraborty, Tanujit & Chakraborty, Ashis Kumar & Murthy, C.A., 2019. "A nonparametric ensemble binary classifier and its statistical properties," Statistics & Probability Letters, Elsevier, vol. 149(C), pages 16-23.
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