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)


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.


Honey yield Weather Radial basis functions Variable screening 



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


  1. 1.
    Anuário de Estadistica del Ministerio de Agricultura, Alimentación y Medio Ambiente.
  2. 2.
    Crane, E.: The Archaeology of Beekeeping. Cornell University Press, Ithaca (1983)Google Scholar
  3. 3.
  4. 4.
    Holmes, W.: Weather and honey yields. Scott. Beekeep. 75, 190–192 (1988)Google Scholar
  5. 5.
    Holmes, W.: The influence of weather on annual yields of honey. J. Agric. Sci. 139, 95–102 (2002)CrossRefGoogle Scholar
  6. 6.
    Hurst, G.W.: Honey production and summer temperatures. Meteorol. Mag. 96, 116–120 (1967)Google Scholar
  7. 7.
    Hurst, G.W.: Temperatures inhigh summer, and honey production. Meteorol. Mag. 99, 75–82 (1970)Google Scholar
  8. 8.
    Instituto de investigación y formación agraria y pesquera.
  9. 9.
    Krishnamurti, B.: A brief analysis of eleven years (1928–1938) records of scale hives at the Rothamsted Bee Laboratory. Bee World 20, 121–123 (1939)CrossRefGoogle Scholar
  10. 10.
    MATLAB 2016a: Natick. The MathWorks Inc., Massachusetts (2016)Google Scholar
  11. 11.
    Nelder, J., Mead, R.: A simplex method for function minimization. Comput. J. 7, 308–313 (1965)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Powell, M.: Radial basis function methods for interpolation to functions of many variables. HERMIS Int. J. Comput. Math. Appl. 3, 1–23 (2002)zbMATHGoogle Scholar
  13. 13.
    Rocha, H., Li, W., Hahn, A.: Principal component regression for fitting wing weight data of subsonic transports. J. Aircr. 43, 1925–1936 (2006)CrossRefGoogle Scholar
  14. 14.
    Rocha, H.: Model parameter tuning by cross validation and global optimization: application to the wing weight fitting problem. Struct. Multi. Optim. 37, 197–202 (2008)CrossRefGoogle Scholar
  15. 15.
    Rocha, H.: On the selection of the most adequate radial basis function. Appl. Math. Model. 33, 1573–1583 (2009)CrossRefGoogle Scholar
  16. 16.
    Rocha, H., Dias, J.M., Ferreira, B.C., Lopes, M.C.: Selection of intensity modulated radiation therapy treatment beam directions using radial basis functions within a pattern search methods framework. J. Global Optim. 57, 1065–1089 (2013)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Rocha, H., Dias, J.M., Ferreira, B.C., Lopes, M.C.: Beam angle optimization for intensity-modulated radiation therapy using a guided pattern search method. Phys. Med. Biol. 58, 2939 (2013)CrossRefGoogle Scholar
  18. 18.
    Switanek, M., Crailsheim, K., Truhetz, H., Brodschneider, R.: Modelling seasonal effects of temperature and precipitation on honey bee winter mortality in a temperate climate. Sci. Total Environ. 579, 1581–1587 (2017)CrossRefGoogle Scholar
  19. 19.
    Tu, J.: Cross-validated multivariate metamodeling methods for physics-based computer simulations. In: Proceedings of the IMAC-XXI (2003)Google Scholar
  20. 20.
    Tu, J., Jones, D.R.: Variable screening in metamodel design by cross-validated moving least squares method. In: Proceedings of the 44th AIAA (2003)Google Scholar
  21. 21.
    Zilinskas, A.: On similarities between two models of global optimization: statistical models and radial basis functions. J. Glob. Optim. 48, 173–182 (2010)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

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

Personalised recommendations