RT Journal Article
T1 A deep learning LSTM-based approach for forecasting annual pollen curves: Olea and Urticaceae pollen types as a case study
A1 Picornell Rodríguez, Antonio
A1 Hurtado-Requena, Sandro José
A1 Antequera-Gómez, María Luisa
A1 Barba-González, Cristóbal
A1 Ruiz-Mata, Rocío
A1 De Gálvez-Montañez, Enrique
A1 Recio-Criado, María Marta
A1 Trigo-Pérez, María del Mar
A1 Aldana-Montes, José Francisco
A1 Navas-Delgado, Ismael
K1 Fisiología vegetal
K1 Botánica
K1 Polen
AB 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.
PB Elsevier
YR 2023
FD 2023-11-16
LK https://hdl.handle.net/10630/29461
UL https://hdl.handle.net/10630/29461
LA eng
NO 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
NO Funding for open Access charge: Universidad de Málaga / CBUA.This work has been partially funded by the Spanish Ministry of Science and Innovation via grant (funded by MCIN/AEI/10.13039/5011 00011033/) PID2020-112540RB-C41, AETHER-UMA (A smart data holistic approach for context-aware data analytics: semantics and context exploitation), and grant ‘‘Environmental and Biodiversity Climate Change Lab (EnBiC2-Lab)’’ LIFEWATCH-2019-11-UMA-01 (AEI/FEDER,UE). A. Picornell has been supported by a postdoctoral grant financedby the Consejería de Transformación Económica, Industria, Conocimiento𝑦 Universidades (Junta de Andalucía, POSTDOC_21_00056).
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026