Forecasting the Start and End of Pollen Season in Madrid

Abstract

In this paper we approach the problem of predicting the start and the end dates for the pollen season of grasses (family Poaceae) and plantains (family Plantago) in the city of Madrid. A classification-based approach is introduced to forecast the main pollination season, and the proposed method is applied to a range of parameters such as the threshold level, which defines the pollen season, and several forecasting horizons. Different computational intelligence approaches are tested including Random Forests, Logistic Regression and Support Vector Machines. The model allows to predict risk exposures for patients and thus anticipate the activation of preventive measures for clinical institutions.