Optimization of artificial neural network models through genetic algorithms for surface ozone concentration forecasting

Pires, JCM; Gonccalves, B; Azevedo, FG; Carneiro, AP; Rego, N; Assembleia, AJB; Lima, JFB; Silva, PA; Alves, C; Martins, FG

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Pires, J., Gonccalves, B., Azevedo, F., Carneiro, A., Rego, N., & Assembleia, A., et al.. (2012). Optimization of artificial neural network models through genetic algorithms for surface ozone concentration forecasting. Environmental Science and Pollution Research, 19(8), 3228–3234.

Resource type: Journal Article
BibTeX citation key: Pires2012
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Categories: Artificial Intelligence, Complexity Science, Computer Science, Data Sciences, General
Subcategories: Big data, Machine recognition, Neural nets
Creators: Alves, Assembleia, Azevedo, Carneiro, Gonccalves, Lima, Martins, Pires, Rego, Silva
Publisher:
Collection: Environmental Science and Pollution Research

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Abstract

This study proposes three methodologies to define artificial neural network models through genetic algorithms (GAs) to predict the next-day hourly average surface ozone (O3) concentrations. GAs were applied to define the activation function in hidden layer and the number of hidden neurons.

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