2026-06-01T16:04:52Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/294612026-02-03T11:02:54Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Picornell Rodríguez, Antonio author Hurtado-Requena, Sandro José author Antequera-Gómez, María Luisa author Barba-González, Cristóbal author Ruiz-Mata, Rocío author De Gálvez-Montañez, Enrique author Recio-Criado, María Marta author Trigo-Pérez, María del Mar author Aldana-Montes, José Francisco author Navas-Delgado, Ismael author 2023-11-16 Airborne pollen can trigger allergic rhinitis and other respiratory diseases in the synthesised population, which makes it one of the most relevant biological contaminants. Therefore, implementing accurate forecast systems is a priority for public health. The current forecast models are generally useful, but they falter when long time series of data are managed. The emergence of new computational techniques such as the LSTM algorithms could constitute a significant improvement for the pollen risk assessment. In this study, several LSTM variants were applied to forecast monthly pollen integrals in Málaga (southern Spain) using meteorological variables as predictors. Olea and Urticaceae pollen types were modelled as proxies of different annual pollen curves, using data from the period 1992–2022. The aims of this study were to determine the LSTM variants with the highest accuracy when forecasting monthly pollen integrals as well as to compare their performance with the traditional pollen forecast methods. The results showed that the CNN-LSTM were the most accurate when forecasting the monthly pollen integrals for both pollen types. Moreover, the traditional forecast methods were outperformed by all the LSTM variants. These findings highlight the importance of implementing LSTM models in pollen forecasting for public health and research applications. Antonio Picornell, Sandro Hurtado, María Luisa Antequera-Gómez, Cristóbal Barba-González, Rocío Ruiz-Mata, Enrique de Gálvez-Montañez, Marta Recio, María del Mar Trigo, José F. Aldana-Montes, Ismael Navas-Delgado, A deep learning LSTM-based approach for forecasting annual pollen curves: Olea and Urticaceae pollen types as a case study, Computers in Biology and Medicine, Volume 168, 2024, 107706, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2023.107706 https://hdl.handle.net/10630/29461 10.1016/j.compbiomed.2023.107706 Fisiología vegetal Botánica Polen A deep learning LSTM-based approach for forecasting annual pollen curves: Olea and Urticaceae pollen types as a case study