Predictive modelling of eutrophication in the Pozón de la Dolores lake (Northern Spain) by using an evolutionary support vector machines approach

• Published in: Journal of mathematical biology Vol. 76; no. 4; pp. 817 - 840
• Main Authors: García-Nieto, P. J., García-Gonzalo, E., Alonso Fernández, J. R., Díaz Muñiz, C.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2018; Springer Nature B.V
• Subjects: Algae; Applications of Mathematics; Chlorophyll; Eutrophication; Lakes; Mathematical and Computational Biology; Mathematical models; Mathematics; Mathematics and Statistics; Nutrients; Optimization techniques; Particle swarm optimization; Performance prediction; Phosphorus; Regression analysis; Support vector machines; Water quality; Eutrophication in water bodies; 62G08; 68T20; Particle swarm optimization (PSO); Support vector machines (SVMs); Regression analysis; 92B20
• ISSN: 0303-6812; 1432-1416
• DOI: 10.1007/s00285-017-1161-2

Eutrophication is a water enrichment in nutrients (mainly phosphorus) that generally leads to symptomatic changes and deterioration of water quality and all its uses in general, when the production of algae and other aquatic vegetations are increased. In this sense, eutrophication has caused a variety of impacts, such as high levels of Chlorophyll a ( Chl -a). Consequently, anticipate its presence is a matter of importance to prevent future risks. The aim of this study was to obtain a predictive model able to perform an early detection of the eutrophication in water bodies such as lakes. This study presents a novel hybrid algorithm, based on support vector machines (SVM) approach in combination with the particle swarm optimization (PSO) technique, for predicting the eutrophication from biological and physical–chemical input parameters determined experimentally through sampling and subsequent analysis in a certificate laboratory. This optimization technique involves hyperparameter setting in the SVM training procedure, which significantly influences the regression accuracy. The results of the present study are twofold. In the first place, the significance of each biological and physical–chemical variables on the eutrophication is presented through the model. Secondly, a model for forecasting eutrophication is obtained with success. Indeed, regression with optimal hyperparameters was performed and coefficients of determination equal to 0.90 for the Total phosphorus estimation and 0.92 for the Chlorophyll concentration were obtained when this hybrid PSO–SVM-based model was applied to the experimental dataset, respectively. The agreement between experimental data and the model confirmed the good performance of the latter.