Abstract
In this paper, we approach the problem of predicting the concentrations of Poaceae pollen which define the main pollination season in the city of Madrid. A classification-based approach, based on a computational intelligence model (random forests), is applied to forecast the dates in which risk concentration levels are to be observed. Unlike previous works, the proposal extends the range of forecasting horizons up to 6 months ahead. Furthermore, the proposed model allows to determine the most influential factors for each horizon, making no assumptions about the significance of the weather features. The performace of the proposed model proves it as a successful tool for allergy patients in preventing and minimizing the exposure to risky pollen concentrations and for researchers to gain a deeper insight on the factors driving the pollination season.
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Acknowledgments
This work has been partially funded by Ministerio de Economía y Competitividad, Gobierno de España, through a Ramón y Cajal grant (RYC-2012-11984).
The authors would like to thank Patricia Cervigón (Comunidad de Madrid) and Montserrat Gutiérrez Bustillo (Universidad Complutense de Madrid) for his assistance in obtaining the data for this study.
The authors would also like to thank the anonymous reviewers for their useful, constructive, and valuable comments, which greatly improved the original version of the manuscript.
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Navares, R., Aznarte, J.L. Predicting the Poaceae pollen season: six month-ahead forecasting and identification of relevant features. Int J Biometeorol 61, 647–656 (2017). https://doi.org/10.1007/s00484-016-1242-8
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DOI: https://doi.org/10.1007/s00484-016-1242-8