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Modeling the optimal dosage of coagulants in water treatment plants using various machine learning models

Author

Listed:
  • Mohammed Achite

    (Hassiba Benbouali University of Chlef)

  • Saeed Farzin

    (Semnan University)

  • Nehal Elshaboury

    (Construction and Project Management Research Institute)

  • Mahdi Valikhan Anaraki

    (Semnan University)

  • Mohammed Amamra

    (Hassiba Benbouali University of Chlef)

  • Abderrezak Kamel Toubal

    (Hassiba Benbouali University of Chlef)

Abstract

One of the main methods for determining coagulant dosage (CD) is the jar test. However, this method is expensive, time-consuming, and requires laboratory equipment. In this situation, machine learning, especially hybrid machine learning, is suitable to estimate the CD without requiring a jar test. To this end, a hybrid model based on a combination of the M5 and the gorilla troops optimizer (GTO) algorithm was introduced as the M5-GTO model. Nine different parameters, including raw water production (RWP), turbidity of water, Conductivity, TDS, Salinity, pH, water temperature (WT), SM, and O2, were also utilized as inputs in the CD modeling. The results of comparing the proposed model with the multiple linear regression, multiple nonlinear regression, artificial neural network, multivariate adaptive regression splines, M5 model tree, k-nearest neighbor, least-squares support vector machine, general regression neural network, and random forest (RF) showed that this model is more accurate in CD modeling. Besides, the M5-GTO well estimated the distribution of observational data. For example, the values of the mean absolute error, root mean square error (RMSE), relative RMSE, normalized RMSE, and correlation coefficient criteria for the M5-GTO were equal to 0.562, 1.172, 0.257, 0.170, and 0.967, which were up to 73%, 54.9%, 54.8%, 55%, and 4% are more accurate than LSSVM (worst algorithm), respectively. In addition, the M5 and RF algorithms were also in the second and third ranks. The partial dependence plots results showed that the RWP and WT had the most significant effects on CD changes. Increasing the RWP reduced the amount of CD, while increasing the WT increased the CD. The algorithm introduced in the present study has a high potential for modeling various parameters in water treatment plants.

Suggested Citation

  • Mohammed Achite & Saeed Farzin & Nehal Elshaboury & Mahdi Valikhan Anaraki & Mohammed Amamra & Abderrezak Kamel Toubal, 2024. "Modeling the optimal dosage of coagulants in water treatment plants using various machine learning models," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(2), pages 3395-3421, February.
    • Handle: RePEc:spr:endesu:v:26:y:2024:i:2:d:10.1007_s10668-022-02835-0
    DOI: 10.1007/s10668-022-02835-0
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    References listed on IDEAS

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    1. Chamanthi Denisha Jayaweera & Norashid Aziz, 2022. "An efficient neural network model for aiding the coagulation process of water treatment plants," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(1), pages 1069-1085, January.
    2. Mahdi Valikhan Anaraki & Saeed Farzin & Sayed-Farhad Mousavi & Hojat Karami, 2021. "Uncertainty Analysis of Climate Change Impacts on Flood Frequency by Using Hybrid Machine Learning Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 199-223, January.
    3. Nagehan İlhan & Ayşegül Demir Yetiş & Mehmet İrfan Yeşilnacar & Ayşe Dilek Sınanmış Atasoy, 2022. "Predictive modelling and seasonal analysis of water quality indicators: three different basins of Şanlıurfa, Turkey," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(3), pages 3258-3292, March.
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