Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/45897
Title: Honey Yield Forecast Using Radial Basis Functions
Authors: Rocha, Humberto 
Dias, Joana 
Keywords: Honey yield; Weather; Radial basis functions; Variable screening
Issue Date: Jan-2018
Publisher: Springer
Serial title, monograph or event: MOD 2017: Machine Learning, Optimization, and Big Data
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.
URI: http://hdl.handle.net/10316/45897
DOI: 10.1007/978-3-319-72926-8_40
Rights: openAccess
Appears in Collections:I&D CeBER - Livros e Capítulos de Livros

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