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

DOI10.1016/J.AMC.2015.03.075zbMath1410.90263OpenAlexW2081911755MaRDI QIDQ1643054

Y. Aharonov

Publication date: 18 June 2018

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2015.03.075

zbMATH Keywords

regression analysisparticle swarm optimization (PSO)support vector machines (SVMs)cyanobacteriacyanotoxinsharmful algal blooms (HABs)

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Ecology (92D40)

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Uses Software

• LIBSVM
• ABC

Cites Work

• Support vector machines and neural networks used to evaluate paper manufactured using \textit{Eucalyptus globulus}
• Handbook of swarm intelligence. Concepts, principles and applications
• Creating a quality map of a slate deposit using support vector machines
• Support-vector networks
• A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm
• Support vector machines: theory and applications.
• Cross-Validation of Regression Models
• Particle Swarm Optimization
• Support Vector Machines
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