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Water eutrophication assessment relied on various machine learning techniques: A case study in the Englishmen Lake (Northern Spain)

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  • García Nieto, P.J.
  • García-Gonzalo, E.
  • Alonso Fernández, J.R.
  • Díaz Muñiz, C.

Abstract

Algal atypical productivity, also called eutrophication, is a process where the phosphorus content in the water, together with aquatic flora, increases, causing high Chlorophyll levels and affecting the water quality and its possible applications. Therefore, it is important to be able to anticipate such circumstance to avoid subsequent hazards. In this paper, a model that estimates the conditions where an abnormal growth of algae in reservoirs and lakes takes place is built. This method combines artificial bee colony and support vector machines algorithms to predict the eutrophication taking into account physical-chemical and biological data sampled in the Englishmen Lake and posterior analysis in a laboratory. The support vector machines parameters are tuned by means of the artificial bee colony algorithm, improving the accuracy of the procedure. For comparison sake, two other methods have been used to construct additional models, the M5 model tree and multilayer perceptron network. Two objectives are covered by this study: the forecasting of the algal proliferation by means of the model and, the ranking of the relative importance of the independent variables. Indeed, coefficients of determination of 0.92 for the Chlorophyll and 0.90 for the Total phosphorus concentrations were obtained using this hybrid method that optimizes the regression parameters. Furthermore, the results obtained with M5 model tree and multilayer perceptron network techniques were clearly worse. Finally, conclusions of this work are drawn in the final section.

Suggested Citation

  • García Nieto, P.J. & García-Gonzalo, E. & Alonso Fernández, J.R. & Díaz Muñiz, C., 2019. "Water eutrophication assessment relied on various machine learning techniques: A case study in the Englishmen Lake (Northern Spain)," Ecological Modelling, Elsevier, vol. 404(C), pages 91-102.
    • Handle: RePEc:eee:ecomod:v:404:y:2019:i:c:p:91-102
    DOI: 10.1016/j.ecolmodel.2019.03.009
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    References listed on IDEAS

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    1. Zeng, Jianwu & Qiao, Wei, 2013. "Short-term solar power prediction using a support vector machine," Renewable Energy, Elsevier, vol. 52(C), pages 118-127.
    2. Ali Rahimikhoob & Maryam Asadi & Mahmood Mashal, 2013. "A Comparison Between Conventional and M5 Model Tree Methods for Converting Pan Evaporation to Reference Evapotranspiration for Semi-Arid Region," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(14), pages 4815-4826, November.
    3. Mohammad Nikoo & Najmeh Mahjouri, 2013. "Water Quality Zoning Using Probabilistic Support Vector Machines and Self-Organizing Maps," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2577-2594, May.
    4. Robert J. Díaz & Rutger Rosenberg, 2011. "Introduction to Environmental and Economic Consequences of Hypoxia," International Journal of Water Resources Development, Taylor & Francis Journals, vol. 27(1), pages 71-82, March.
    5. Mahesh Pal & Arun Goel, 2007. "Estimation of Discharge and End Depth in Trapezoidal Channel by Support Vector Machines," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(10), pages 1763-1780, October.
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    Cited by:

    1. Jin‐Won Yu & Ju‐Song Kim & Yun‐Chol Jong & Xia Li & Gwang‐Il Ryang, 2022. "Forecasting chlorophyll‐a concentration using empirical wavelet transform and support vector regression," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1691-1700, December.

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    More about this item

    Keywords

    Support vector machine (SVM); Artificial bee colony (ABC); Artificial neural networks (ANNs); M5 model tree; Algal atypical productivity in lakes; Regression analysis;
    All these keywords.

    JEL classification:

    • M5 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics

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