title: What are the most important variables for Poaceae airborne pollen forecasting?
abstract: In this paper, the problem of predicting future concentrations of airborne pollen is solved through a computational intelligence data-driven approach. The proposed method is able to identify the most important variables among those considered by other authors (mainly recent pollen concentrations and weather parameters), without any prior assumptions about the phenological relevance of the variables. Furthermore, an inferential procedure based on non-parametric hypothesis testing is presented to provide statistical evidence of the results, which are coherent to the literature and outperform previous proposals in terms of accuracy. The study is built upon Poaceae airborne pollen concentrations recorded in seven different locations across the Spanish province of Madrid.
authors.affiliation: Superior Technical School of Computer Engineering, UNED, Juan del Rosal, 16, Madrid 28040, Spain.
authors.forename: Ricardo
authors.initials: R
authors.lastname: Navares
authors.affiliation: Department of Artificial Intelligence, UNED, Juan del Rosal, 16, Madrid 28040, Spain. Electronic address: jlaznarte@dia.uned.es.
authors.forename: José Luis
authors.initials: JL
authors.lastname: Aznarte
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000706:statistics & numerical data*; D000485:Allergens / Q000032:analysis*; D004784:Environmental Monitoring / Q000379:methods*; D005544:Forecasting; D006109:Poaceae; D011058:Pollen; D013030:Spain; D014887:Weather
chemical_list: D000393:Air Pollutants; D000485:Allergens
keywords: Aerobiology; Feature selection; Madrid; Nonparametric tests; Prediction; Random forests; Time series