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Support Vector Machines and Multilayer Perceptron Networks Used to Evaluate the Cyanotoxins Presence from Experimental Cyanobacteria Concentrations in the Trasona Reservoir (Northern Spain)

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Abstract

Cyanobacteria also known as blue-green algae can be found in almost every conceivable environment. 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. Consequently, anticipation of cyanotoxins presence is a matter of importance to prevent risks. The aim of this study is to build a cyanotoxin diagnostic model by using support vector machines and multilayer perceptron networks from cyanobacterial concentrations determined experimentally in the Trasona reservoir (recreational reservoir used as a high performance training centre of canoeing in the Northern Spain). The results of the present study are two-fold. In the first place, the significance of each biological and physical-chemical variables on the cyanotoxins presence in the reservoir is presented through the model. Secondly, a predictive model able to forecast the possible presence of cyanotoxins is obtained. The agreement of the model with experimental data confirmed its good performance. Finally, conclusions of this innovative research work are exposed.

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Acknowledgements

Authors wish to acknowledge the computational support provided by the Department of Mathematics at University of Oviedo as well as pollutant data in the Trasona Reservoir of Avilés (Northern Spain) supplied by the Cantabrian Basin Authority (Ministry of Environment, Rural and Marine Affairs of Spain). This paper has been funded by the Government of the Principality of Asturias through funds from the Programme of Science, Technology and Innovation (PCTI) of Asturias 2006–2009, co-financed by 80 % within the priority Focus 1 of the Operational Programme FEDER of the Principality of Asturias 2007–2013 (Research project FC-11-PC10-19). English grammar and spelling of the manuscript have been revised by Anthony Ashworth, a teacher and an international lecturer.

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of Vigo, 36200, Vigo, Spain

    J. A. Vilán Vilán

  2. Cantabrian Basin Authority, Spanish Ministry of Agriculture, Food and Environment, 33071, Oviedo, Spain

    J. R. Alonso Fernández & C. Díaz Muñiz

  3. Department of Mathematics, Faculty of Sciences, University of Oviedo, 33007, Oviedo, Spain

    P. J. García Nieto

  4. Department of Construction and Manufacturing Engineering, University of Oviedo, 33204, Gijón, Spain

    F. Sánchez Lasheras

  5. Mining Exploitation and Prospecting Department, University of Oviedo, 33004, Oviedo, Spain

    F. J. de Cos Juez

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  1. J. A. Vilán Vilán
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  2. J. R. Alonso Fernández
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  3. P. J. García Nieto
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  4. F. Sánchez Lasheras
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  5. F. J. de Cos Juez
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  6. C. Díaz Muñiz
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Correspondence to P. J. García Nieto.

Appendix A

Appendix A

Supplementary site-specific experimental data associated with this article can be found at http://dl.dropbox.com/u/36679320/Trasona_reservoir_data_sc.xls.

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Vilán Vilán, J.A., Alonso Fernández, J.R., García Nieto, P.J. et al. Support Vector Machines and Multilayer Perceptron Networks Used to Evaluate the Cyanotoxins Presence from Experimental Cyanobacteria Concentrations in the Trasona Reservoir (Northern Spain). Water Resour Manage 27, 3457–3476 (2013). https://doi.org/10.1007/s11269-013-0358-4

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