An Amazon stingless bee foraging activity predicted using recurrent artificial neural networks and attribute selection
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An Amazon stingless bee foraging activity predicted using recurrent artificial neural networks and attribute selection.pdf
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2.31 MB
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Author(s)
Gomes, Pedro A. B.
Suhara, Yoshihiko
Nunes-Silva, Patrícia
Costa, Luciano
Arruda, Helder
Venturieri, Giorgio
Imperatriz-Fonseca, Vera Lucia
Pentland, Alex
de Souza, Paulo
Pessin, Gustavo
Date Issued
January 8, 2020
Publisher
Scientific Reports
Citation
Gomes, P. A., Suhara, Y., Nunes-Silva, P., Costa, L., Arruda, H., Venturieri, G., ... & Pessin, G. (2020). An Amazon stingless bee foraging activity predicted using recurrent artificial neural networks and attribute selection. Scientific reports, 10(1), 1-12.
Abstract
Bees play a key role in pollination of crops and in diverse ecosystems. There have been multiple reports in recent years illustrating bee population declines worldwide. The search for more accurate forecast models can aid both in the understanding of the regular behavior and the adverse situations that may occur with the bees. It also may lead to better management and utilization of bees as pollinators. We address an investigation with Recurrent Neural Networks in the task of forecasting bees’ level of activity taking into account previous values of level of activity and environmental data such as temperature, solar irradiance and barometric pressure. We also show how different input time windows, algorithms of attribute selection and correlation analysis can help improve the accuracy of our model.
MIT Department
MIT Connection Science (Research institute)
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