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Modeling of the algal atypical increase in La Barca reservoir using the DE optimized least square support vector machine approach with feature selection

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

Abstract

Algal atypical increase is a consequence of water fertilization (also called eutrophication) and a worldwide environmental concern since water quality and its uses are seriously compromised. The effects of this abnormal proliferation are varied. Among them, and the most direct one is the increase of Chlorophyll a (Chl-a) concentration. Prevention is the most effective measure given that once the algal proliferation starts it is too difficult and costly to stop the process and thus, prediction is essential. A new hybrid algorithm that combines least square support vector machines (LS-SVM) with a radial basis function (RBF) kernel and differential evolution (DE) optimization technique to estimate the algal abnormal proliferation from physical–chemical and biological variables has been constructed here. This technique involves the optimization of the LS-SVM hyperparameters during the training process. Additionally, we have carried out a variable (feature) selection of the model using backward and forward selection processes with success and from this optimized model, the relative importance of the variables was estimated. For comparison purposes, binary PSO and SA were also used in feature selection. Thus, apart from successfully forecasting algal atypical growth (coefficients of determination 0.90 and 0.95 for the Chlorophyll and Total Phosphorus, respectively), the model showed here can establish the significance of each biological and physical–chemical parameter of the algal enhanced growth.

Suggested Citation

  • García-Nieto, P.J. & García-Gonzalo, E. & Fernández, J.R. Alonso & Muñiz, C. Díaz, 2019. "Modeling of the algal atypical increase in La Barca reservoir using the DE optimized least square support vector machine approach with feature selection," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 166(C), pages 461-480.
    • Handle: RePEc:eee:matcom:v:166:y:2019:i:c:p:461-480
    DOI: 10.1016/j.matcom.2019.07.011
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    References listed on IDEAS

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    1. Yuan, Xiaohui & Tan, Qingxiong & Lei, Xiaohui & Yuan, Yanbin & Wu, Xiaotao, 2017. "Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine," Energy, Elsevier, vol. 129(C), pages 122-137.
    2. 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.
    3. Zhu, Bangzhu & Han, Dong & Wang, Ping & Wu, Zhanchi & Zhang, Tao & Wei, Yi-Ming, 2017. "Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression," Applied Energy, Elsevier, vol. 191(C), pages 521-530.
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