Machine Learning Regression to Predict Pollen Concentrations of Oleaceae and Quercus Taxa in Thessaloniki, Greece

Abstract

Airborne pollen triggers allergic reactions in up to 40% of the global population. The incidence of pollen allergies is increasing in Thessaloniki, Greece and it is predicted that more than 50% of the European Union’s inhabitants will suffer from allergic rhinitis by 2025. Thus, it is essential to investigate and predict high pollen concentrations to address this growing concern. This study utilized the Gradient Boosting Regression (GBR) technique, a machine learning approach, to estimate pollen concentrations of Oleaceae and Quercus taxa, using daily meteorological and land surface data obtained from the European Center for Medium-Range Weather Forecasts (ECMWF). The method accurately predicted pollen concentrations for both species, with an Index of Agreement (IoA) of 0.86 for Oleaceae and 0.78 for Quercus, despite the limited size of the dataset.

Keywords

• airborne pollen
• pollen concentrations
• Oleaceae
• Quercus
• machine learning
• Gradient Boosting Regression