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A New Predictive Model for Evaluating Chlorophyll-a Concentration in Tanes Reservoir by Using a Gaussian Process Regression

Author

Listed:
  • Paulino José García-Nieto

    (University of Oviedo)

  • Esperanza García-Gonzalo

    (University of Oviedo)

  • José Ramón Alonso Fernández

    (Spanish Ministry for the Ecological Transition and Demographic Challenge)

  • Cristina Díaz Muñiz

    (Spanish Ministry for the Ecological Transition and Demographic Challenge)

Abstract

Chlorophyll-a (hereafter referred to as Chl-a) is a recognized indicator for phytoplankton abundance and biomass –hence, an effective estimation of the trophic condition– of water bodies as lakes, reservoirs and oceans. Indeed, Chl-a is the primary molecule responsible for photosynthesis. A strong and robust Bayesian nonparametric technique, termed Gaussian process regression (GPR) approach, for foretelling the dependent variable Chl-a concentration in Tanes reservoir from a dataset concerning to 268 samples is shown in this paper. Ten years (2006–2015) of monitoring water quality variables (biological and physico-chemical independent variables) in the Tanes reservoir were used to build this mathematical GPR-relied model. As an optimizer, the method known as Limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGSB) iterative algorithm was used; this allows the selection of kernel optimal parameters during the GPR training phase, which greatly determines the regression precision. The results of the current investigation can be summarized in two. Firstly, the relevance of each input variable on Chl-a concentration in Tanes reservoir is determined. Secondly, the Chl-a can be successfully predicted using this hybrid LBFGSB/GPR–relied model (R2 and r values were 0.8597 and 0.9306, respectively). The concordance between observed data and the model clearly proves the high efficiency of this innovative approach.

Suggested Citation

  • Paulino José García-Nieto & Esperanza García-Gonzalo & José Ramón Alonso Fernández & Cristina Díaz Muñiz, 2020. "A New Predictive Model for Evaluating Chlorophyll-a Concentration in Tanes Reservoir by Using a Gaussian Process Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4921-4941, December.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:15:d:10.1007_s11269-020-02699-x
    DOI: 10.1007/s11269-020-02699-x
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    References listed on IDEAS

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    1. Cai, Haoshu & Jia, Xiaodong & Feng, Jianshe & Li, Wenzhe & Hsu, Yuan-Ming & Lee, Jay, 2020. "Gaussian Process Regression for numerical wind speed prediction enhancement," Renewable Energy, Elsevier, vol. 146(C), pages 2112-2123.
    2. Marie-Pier Schinck & Chloé L’Ecuyer-Sauvageau & Justin Leroux & Charlène Kermagoret & Jérôme Dupras, 2020. "Risk, Drinking Water and Harmful Algal Blooms: A Contingent Valuation of Water Bans," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(12), pages 3933-3947, September.
    3. J. Vilán Vilán & J. Alonso Fernández & P. García Nieto & F. Sánchez Lasheras & F. de Cos Juez & C. Díaz Muñiz, 2013. "Support Vector Machines and Multilayer Perceptron Networks Used to Evaluate the Cyanotoxins Presence from Experimental Cyanobacteria Concentrations in the Trasona Reservoir (Northern Spain)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(9), pages 3457-3476, July.
    4. Li, Meng & Sadoughi, Mohammadkazem & Hu, Zhen & Hu, Chao, 2020. "A hybrid Gaussian process model for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
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    Cited by:

    1. Wu, Jiawei & Wan, Liangqi, 2024. "Reliability sensitivity analysis for RBSMC: A high-efficiency multiple response Gaussian process model," Reliability Engineering and System Safety, Elsevier, vol. 243(C).

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