A deep learning LSTM-based approach for forecasting annual pollen curves: Olea and Urticaceae pollen types as a case study.

Autor: Picornell A; Department of Botany and Plant Physiology, University of Malaga, Malaga 29071, Spain. Electronic address: picornell@uma.es., Hurtado S; Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga 29071, Spain. Electronic address: sandrohr@uma.es., Antequera-Gómez ML; Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga 29071, Spain. Electronic address: marialan@uma.es., Barba-González C; Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga 29071, Spain. Electronic address: cbarba@uma.es., Ruiz-Mata R; Department of Botany and Plant Physiology, University of Malaga, Malaga 29071, Spain. Electronic address: roruizmata@uma.es., de Gálvez-Montañez E; Department of Botany and Plant Physiology, University of Malaga, Malaga 29071, Spain. Electronic address: kikedegalvez@uma.es., Recio M; Department of Botany and Plant Physiology, University of Malaga, Malaga 29071, Spain. Electronic address: martarc@uma.es., Trigo MDM; Department of Botany and Plant Physiology, University of Malaga, Malaga 29071, Spain. Electronic address: aerox@uma.es., Aldana-Montes JF; Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga 29071, Spain. Electronic address: jfaldana@uma.es., Navas-Delgado I; Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga 29071, Spain. Electronic address: ismael@uma.es.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2024 Jan; Vol. 168, pp. 107706. Date of Electronic Publication: 2023 Nov 16.
DOI: 10.1016/j.compbiomed.2023.107706
Abstrakt: 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.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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