A hybrid PSO optimized SVM-based method for predicting of the cyanotoxin content from experimental cyanobacteria concentrations in the Trasona reservoir: A case study in Northern Spain

P.J. García Nieto, J.R. Alonso Fernández, V.M. González Suárez, C. Díaz Muñiz, E. García-Gonzalo and R. Mayo Bayón

Applied Mathematics and Computation, 2015, vol. 260, issue C, 170-187

Abstract: There is an increasing need to describe cyanobacteria blooms since some cyanobacteria produce toxins termed cyanotoxins and, as a result, anticipate its presence is a matter of importance to prevent risks. Cyanobacteria blooms occur frequently and globally in water bodies, and they are a major concern in terms of their effects on other species such as plants, fish and other microorganisms, but especially by the possible acute and chronic effects on human health due to the potential danger from cyanobacterial toxins produced by some of them in recreational or drinking waters. Therefore, the aim of this study is to build a cyanotoxin diagnostic model by using support vector machines (SVMs) in combination with the particle swarm optimization (PSO) technique from cyanobacterial concentrations determined experimentally in the Trasona reservoir (recreational reservoir used as a high performance training center of canoeing in the Northern Spain). The Trasona reservoir is near Aviles estuary and after a short tour, the brackish waters of the Aviles estuary empty into the Cantabrian sea. This optimization technique involves kernel parameter setting in the SVM training procedure, which significantly influences the regression accuracy. Bearing this in mind, cyanotoxin contents have been predicted here by using the hybrid PSO–SVM-based model from the remaining measured water quality parameters (input variables) in the Trasona reservoir (Northern Spain) with success. In other words, the results of the present study are two-fold. In the first place, the significance of each biological and physical–chemical variable on the cyanotoxin content in the reservoir is presented through the model. Second, a predictive model able to forecast the possible presence of cyanotoxins is obtained. The agreement of the PSO–SVM-based model with experimental data confirmed its good performance. Finally, conclusions of this innovative research work are exposed.

Keywords: Support vector machines (SVMs); Particle swarm optimization (PSO); Harmful algal blooms (HABs); Cyanotoxins; Cyanobacteria; Regression analysis (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:260:y:2015:i:c:p:170-187

DOI: 10.1016/j.amc.2015.03.075

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