RETRACTED:Pollen risk levels prediction from multi-source historical data

Abstract

Numerous studies show that meteorological conditions have an impact on the emission, dispersion and suspension of pollens in the air. Several allergenic species permanently threaten the health of millions of people in France and that can be extrapolate that this is the case in most part of the world. Hence, preventive information on the risk of pollen exposure would become a real asset for allergy sufferers. The main objective of this article is to study, through statistical learning techniques exploiting historical data and meteorological parameters of the day ( T ), the ability to predict three-day ahead ( T + 3 ) pollens presence risk levels in the air on a given territory (in metropolitan France). We are interested in the prediction of risk, discretized in four levels for three families of pollens which are among the most allergenic species (ragweed, cupressaceae and grasses). Combining binary logistic regression models for each risk level using a set of ranking rules or a random forest classifier is proposed in this study. The pollen risk level prediction performances reach 70% to more than 90% of auc, precision and recall on the majority of 68 considered sites and especially with a similar prediction capacity on sites with no previous pollen data. The comparative study with some more classical models of the literature shows that the proposed model have a slight performance advantage. • Pollen risk levels prediction from minimal meteorological information. • Aggregation of binary logistic regression models. • Learning with multi-source spatial and temporal data. • Ability to generalize prediction in space