Comparison of Nitrogen Dioxide Predictions During a Pandemic and Non-pandemic Scenario in the City of Madrid using a Convolutional LSTM Network
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Título
Comparison of Nitrogen Dioxide Predictions During a Pandemic and Non-pandemic Scenario in the City of Madrid using a Convolutional LSTM NetworkFecha de publicación
2022-06-22Editor
World ScientificCita bibliográfica
Iskandaryan, D., Ramos, F., & Trilles, S. (2022). Comparison of Nitrogen Dioxide Predictions During a Pandemic and Non-pandemic Scenario in the City of Madrid using a Convolutional LSTM Network. International Journal of Computational Intelligence and Applications, 21(02), 2250014.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.worldscientific.com/doi/10.1142/S1469026822500146Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
Traditionally, machine learning technologies with the methods and capabilities available, combined with a geospatial dimension, can perform predictive analyzes of air quality with greater accuracy. However, air pollution ... [+]
Traditionally, machine learning technologies with the methods and capabilities available, combined with a geospatial dimension, can perform predictive analyzes of air quality with greater accuracy. However, air pollution is influenced by many external factors, one of which has recently been caused by the restrictions applied to curb the relentless advance of COVID-19. These sudden changes in air quality levels can negatively influence current forecasting models. This work compares air pollution forecasts during a pandemic and non-pandemic period under the same conditions. The ConvLSTM algorithm was applied to predict the concentration of nitrogen dioxide using data from the air quality and meteorological stations in Madrid. The proposed model was applied for two scenarios: pandemic (January–June 2020) and non-pandemic (January–June 2019), each with sub-scenarios based on time granularity (1-h, 12-h, 24-h and 48-h) and combination of features. The Root Mean Square Error was taken as the estimation metric, and the results showed that the proposed method outperformed a reference model, and the feature selection technique significantly improved the overall accuracy. [-]
Publicado en
International Journal of Computational Intelligence and Applications, (2022). Vol. 21, No. 02, 2250014Entidad financiadora
Universitat Jaume I | Ministerio de Ciencia, Innovación y Universidades (Spain) | Generalitat Valenciana
Código del proyecto o subvención
PREDOC/2018/61 | IJC2018-035017-I | GV/2020/035
Derechos de acceso
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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