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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)

Author

Listed:
  • J. Vilán Vilán
  • J. Alonso Fernández
  • P. García Nieto
  • F. Sánchez Lasheras
  • F. de Cos Juez
  • C. Díaz Muñiz

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. Copyright Springer Science+Business Media Dordrecht 2013

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:27:y:2013:i:9:p:3457-3476
    DOI: 10.1007/s11269-013-0358-4
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    Citations

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    Cited by:

    1. Chang-ming Ji & Ting Zhou & Hai-tao Huang, 2014. "Operating Rules Derivation of Jinsha Reservoirs System with Parameter Calibrated Support Vector Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2435-2451, July.
    2. Nieto, P.J. García & Fernández, J.R. Alonso & Suárez, V.M. González & Muñiz, C. Díaz & García-Gonzalo, E. & Bayón, R. Mayo, 2015. "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," Applied Mathematics and Computation, Elsevier, vol. 260(C), pages 170-187.
    3. García Nieto, P.J. & García-Gonzalo, E. & Sánchez Lasheras, F. & de Cos Juez, F.J., 2015. "Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 219-231.
    4. Yunfeng Xu & Chunzi Ma & Shouliang Huo & Dayi Zhang & Zhiping Xu & Guangren Qian & Beidou Xi, 2014. "Establishing Reference Conditions for Lake Water Quality: A Novel Extrapolation Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(8), pages 2161-2178, June.
    5. Fidel Díez Díaz & Fernando Sánchez Lasheras & Víctor Moreno & Ferran Moratalla-Navarro & Antonio José Molina de la Torre & Vicente Martín Sánchez, 2021. "GASVeM: A New Machine Learning Methodology for Multi-SNP Analysis of GWAS Data Based on Genetic Algorithms and Support Vector Machines," Mathematics, MDPI, vol. 9(6), pages 1-19, March.
    6. 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.

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