IDEAS home Printed from https://ideas.repec.org/a/spr/opsear/v56y2019i4d10.1007_s12597-019-00386-z.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12597-019-00386-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s12597-019-00386-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    2. 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.
    3. Vlontzos, G. & Pardalos, P.M., 2017. "Assess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 155-162.
    4. Sayiter Yıldız & Can Bülent Karakuş, 2020. "Estimation of irrigation water quality index with development of an optimum model: a case study," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(5), pages 4771-4786, June.
    5. Mehmet Kayakuş, 2020. "The Estimation of Turkey's Energy Demand Through Artificial Neural Networks and Support Vector Regression Methods," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 8(2), pages 227-236, December.
    6. Işık, Erdem & Inallı, Mustafa, 2018. "Artificial neural networks and adaptive neuro-fuzzy inference systems approaches to forecast the meteorological data for HVAC: The case of cities for Turkey," Energy, Elsevier, vol. 154(C), pages 7-16.
    7. Iqbal Murtza & Jin-Young Kim & Muhammad Adnan, 2024. "Predicting the Performance of Ensemble Classification Using Conditional Joint Probability," Mathematics, MDPI, vol. 12(16), pages 1-16, August.
    8. Kichul Jung & Deg-Hyo Bae & Myoung-Jin Um & Siyeon Kim & Seol Jeon & Daeryong Park, 2020. "Evaluation of Nitrate Load Estimations Using Neural Networks and Canonical Correlation Analysis with K-Fold Cross-Validation," Sustainability, MDPI, vol. 12(1), pages 1-17, January.
    9. Mohammad Rezaie-Balf & Zahra Zahmatkesh & Sungwon Kim, 2017. "Soft Computing Techniques for Rainfall-Runoff Simulation: Local Non–Parametric Paradigm vs. Model Classification Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 3843-3865, September.
    10. Yumin Wang & Weijian Ran & Lei Wu & Yifeng Wu, 2019. "Assessment of River Water Quality Based on an Improved Fuzzy Matter-Element Model," IJERPH, MDPI, vol. 16(15), pages 1-11, August.
    11. Thiago Victor Medeiros Nascimento & Celso Augusto Guimarães Santos & Camilo Allyson Simões Farias & Richarde Marques Silva, 2022. "Monthly Streamflow Modeling Based on Self-Organizing Maps and Satellite-Estimated Rainfall Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2359-2377, May.
    12. Thendiyath Roshni & Madan K. Jha & Ravinesh C. Deo & A. Vandana, 2019. "Development and Evaluation of Hybrid Artificial Neural Network Architectures for Modeling Spatio-Temporal Groundwater Fluctuations in a Complex Aquifer System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(7), pages 2381-2397, May.
    13. Bhattacharjee, Natalia V. & Tollner, Ernest W., 2016. "Improving management of windrow composting systems by modeling runoff water quality dynamics using recurrent neural network," Ecological Modelling, Elsevier, vol. 339(C), pages 68-76.
    14. Ranković, Vesna & Radulović, Jasna & Radojević, Ivana & Ostojić, Aleksandar & Čomić, Ljiljana, 2010. "Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia," Ecological Modelling, Elsevier, vol. 221(8), pages 1239-1244.
    15. Linlin Zhao & Jasper Mbachu & Zhansheng Liu, 2019. "Exploring the Trend of New Zealand Housing Prices to Support Sustainable Development," Sustainability, MDPI, vol. 11(9), pages 1-18, April.
    16. Junguo, Hu & Guomo, Zhou & Xiaojun, Xu, 2013. "Using an improved back propagation neural network to study spatial distribution of sunshine illumination from sensor network data," Ecological Modelling, Elsevier, vol. 266(C), pages 86-96.
    17. Minhao Zhang & Zhiyu Zhang & Xuan Wang & Zhenliang Liao & Lijin Wang, 2024. "The Use of Attention-Enhanced CNN-LSTM Models for Multi-Indicator and Time-Series Predictions of Surface Water Quality," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(15), pages 6103-6119, December.
    18. Shanshan Wang & Joe Wiart, 2020. "Sensor-Aided EMF Exposure Assessments in an Urban Environment Using Artificial Neural Networks," IJERPH, MDPI, vol. 17(9), pages 1-15, April.
    19. West, David & Dellana, Scott, 2011. "An empirical analysis of neural network memory structures for basin water quality forecasting," International Journal of Forecasting, Elsevier, vol. 27(3), pages 777-803, July.
    20. Rana Muhammad Adnan & Hong-Liang Dai & Reham R. Mostafa & Kulwinder Singh Parmar & Salim Heddam & Ozgur Kisi, 2022. "Modeling Multistep Ahead Dissolved Oxygen Concentration Using Improved Support Vector Machines by a Hybrid Metaheuristic Algorithm," Sustainability, MDPI, vol. 14(6), pages 1-23, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:opsear:v:56:y:2019:i:4:d:10.1007_s12597-019-00386-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.