Evaluation of Deep Learning Models for Predicting the Concentration of Air Pollutants in Urban Environments

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-09-15T00:29:42Z
Fecha de publicación2024-01-01
ResumenAir pollution is an issue of great concern globally due to the risks to the health of humanity, animals, and ecosystems. On the one hand, air quality monitoring systems allow for determining the concentration level of air pollutants and health risks through an air quality index (AQI). On the other hand, accurate future predictions of air pollutant concentration levels can provide valuable information for data-driven decision-making to reduce health risks from short- and long-term exposure when indicators exceed permissible limits. In this paper, five deep learning architectures are evaluated to predict the concentration of particulate matter pollutants (in their fractions PM2.5 and PM10) and carbon monoxide (CO) in consecutive hours. The proposed prediction models are based on recurrent neural networks (RNNs), long short-term memory (LSTM), vanilla LSTM, Stacked LSTM, Bi-LSTM, and encoder–decoder LSTM networks. Moreover, a methodology is presented to guide the construction of the prediction model, encompassing raw data processing, model design and optimization, and neural network training, testing, and evaluation. The results underscore the precision and reliability of the Stacked LSTM model in predicting the hourly concentration level for PM2.5, with an RMSE of 3.4538 μg/m3. Similarly, the encoder–decoder LSTM model accurately predicts the concentration level for PM10 and CO, with an RMSE of 3.2606 μg/m3 and 2.1510 ppm, respectively. These evaluations, with their minimal differences in error metrics and coefficient of determination, validate the effectiveness and superiority of the deep learning models over other reference models, instilling confidence in their potential.es_ES
Doihttps://doi.org/10.3390/su16167062es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/1694
Idiomaeses_ES
EditorialMDPI AGes_ES
RelaciónSustainabilityes_ES
URL relacionadohttps://doi.org/10.3390/su16167062es_ES
DerechosAcceso abierto (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_abf2es_ES
FuenteSustainability
TítuloEvaluation of Deep Learning Models for Predicting the Concentration of Air Pollutants in Urban Environmentses_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorTello-Leal, Edgar
AutorRamirez-Alcocer, Ulises Manuel
AutorMacías-Hernández, Bárbara A.
AutorHernandez-Resendiz, Jaciel David
AutorTello-Leal, Edgares_ES
AutorRamirez-Alcocer, Ulises Manueles_ES
AutorMacías-Hernández, Bárbara A.es_ES
AutorHernandez-Resendiz, Jaciel Davides_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Número16es_ES
Rango de páginas7062es_ES
URL relacionadahttps://doi.org/10.3390/su16167062
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen16es_ES

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