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

Paulino José García-Nieto (), Esperanza García-Gonzalo, José Ramón Alonso Fernández () and Cristina Díaz Muñiz
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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

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2020, vol. 34, issue 15, No 22, 4941 pages

Abstract: 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.

Keywords: Chlorophyll-a; Gaussian process regression (GPR); Bayesian statistics; Regression analysis; Reservoir water quality (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11269-020-02699-x

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