Machine Learning to Forecast Airborne Parietaria Pollen in the North-West of the Iberian Peninsula

Gonzalo Astray (), Rubén Amigo Fernández, María Fernández-González, Duarte A. Dias-Lorenzo, Guillermo Guada and Francisco Javier Rodríguez-Rajo
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Gonzalo Astray: Departamento de Química Física, Facultade de Ciencias, Universidade de Vigo, 32004 Ourense, Spain
Rubén Amigo Fernández: Departamento de Bioloxía Vexetal e Ciencias do Solo, Facultade de Ciencias, Universidade de Vigo, 32004 Ourense, Spain
María Fernández-González: Departamento de Bioloxía Vexetal e Ciencias do Solo, Facultade de Ciencias, Universidade de Vigo, 32004 Ourense, Spain
Duarte A. Dias-Lorenzo: Departamento de Bioloxía Vexetal e Ciencias do Solo, Facultade de Ciencias, Universidade de Vigo, 32004 Ourense, Spain
Guillermo Guada: Departamento de Bioloxía Vexetal e Ciencias do Solo, Facultade de Ciencias, Universidade de Vigo, 32004 Ourense, Spain
Francisco Javier Rodríguez-Rajo: Departamento de Bioloxía Vexetal e Ciencias do Solo, Facultade de Ciencias, Universidade de Vigo, 32004 Ourense, Spain

Sustainability, 2025, vol. 17, issue 4, 1-23

Abstract: Pollen forecasting models are helpful tools to predict environmental processes and allergenic risk events. Parietaria belongs to the Urticaceae family, and due to its high-level pollen production, is responsible for many cases of severe pollinosis reactions. This research aims to develop different machine learning models such as the random forest—RF, support vector machine—SVM, and artificial neural network—ANN models, to predict Parietaria pollen concentrations in the atmosphere of northwest Spain using 24 years of data from 1999 to 2022. The results obtained show an increase in the duration and intensity of the Parietaria main pollen season in the Mediterranean region (Ourense). Machine learning models exhibited their capacity to forecast Parietaria pollen concentrations at one, two, and three days ahead. The best selected models presented high correlation coefficients between 0.713 and 0.859, with root mean squared errors between 5.55 and 7.66 pollen grains·m −3 for the testing phase. The models developed could be improved by increasing the number of years, studying other hyperparameter ranges, or analyzing different data distributions.

Keywords: main pollen season; allergenic pollen; random forest; support vector machine; artificial neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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