A hybrid wavelet kernel SVM-based method using artificial bee colony algorithm for predicting the cyanotoxin content from experimental cyanobacteria concentrations in the Trasona reservoir (northern Spain)

DOI10.1016/j.cam.2016.01.045zbMath1404.92216OpenAlexW2260152495MaRDI QIDQ313682

Mohammad Hasan, M. Dambrine

Publication date: 12 September 2016

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

Full work available at URL: https://doi.org/10.1016/j.cam.2016.01.045

zbMATH Keywords

regression analysisartificial bee colony (ABC)Microcystis aeruginosasupport vector machines (SVMs)wavelet kernelWoronichinia naegeliana

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Numerical methods for wavelets (65T60) Ecology (92D40)

Related Items (2)

Editorial: Mathematical modeling and computational methods ⋮ A hybrid DE optimized wavelet kernel SVR-based technique for algal atypical proliferation forecast in La Barca reservoir: a case study

Uses Software

• LIBSVM
• ABC

Cites Work

• Support vector machines and neural networks used to evaluate paper manufactured using \textit{Eucalyptus globulus}
• A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm
• Cross-Validation of Regression Models
• Particle Swarm Optimization
• Support Vector Machines
• The wavelet transform, time-frequency localization and signal analysis
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