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Honey Yield Forecast Using Radial Basis Functions

  • Humberto RochaEmail author
  • Joana Dias
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10710)

Abstract

Honey yields are difficult to predict and have been usually associated with weather conditions. Although some specific meteorological variables have been associated with honey yields, the reported relationships concern a specific geographical region of the globe for a given time frame and cannot be used for different regions, where climate may behave differently. In this study, Radial Basis Function (RBF) interpolation models were used to explore the relationships between weather variables and honey yields. RBF interpolation models can produce excellent interpolants, even for poorly distributed data points, capable of mimicking well unknown responses providing reliable surrogates that can be used either for prediction or to extract relationships between variables. The selection of the predictors is of the utmost importance and an automated forward-backward variable screening procedure was tailored for selecting variables with good predicting ability. Honey forecasts for Andalusia, the first Spanish autonomous community in honey production, were obtained using RBF models considering subsets of variables calculated by the variable screening procedure.

Keywords

Honey yield Weather Radial basis functions Variable screening 

Notes

Acknowledgements

This work has been supported by the Fundação para a Ciência e a Tecnologia (FCT) under project grant UID/MULTI/00308/2013.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Faculdade de Economia, CeBERUniversidade de CoimbraCoimbraPortugal
  2. 2.INESC-CoimbraCoimbraPortugal

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