article.page.titleprefix
Forecasting Air Quality in Tripoli: An Evaluation of Deep Learning Models for Hourly PM2.5 Surface Mass Concentrations

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dc.contributor.authorEsager, Marwa Winis Misbah
dc.contributor.authorÜnlü, Kamil Demirberk
dc.date.accessioned2023-12-08T07:30:35Z
dc.date.available2023-12-08T07:30:35Z
dc.date.issued2023-02-28
dc.descriptionOpen Access Atmosphere 2023, 14(3), 478; https://doi.org/10.3390/atmos14030478 Marwa Winis Misbah Esager, Graduate School of Natural and Applied Sciences, Atilim University, Ankara, Turkey, Kamil Demirberk Ünlü Department of Industrial Engineering, Atilim University, Ankara, Turkey; demirberk.unlu@atilim.edu.tr
dc.description.abstractIn this article, we aimed to study the forecasting of hourly PM2.5 surface mass concentrations in the city of Tripoli, Libya. We employed three state-of-the-art deep learning models, namely long short-term memory, gated recurrent unit, and convolutional neural networks, to forecast PM2.5 levels using univariate time series methodology. Our results revealed that the convolutional neural networks model performed the best, with a coefficient of variation of 99% and a mean absolute percentage error of 0.04. These findings provide valuable insights into the use of deep learning models for forecasting PM2.5 and can inform decision-making regarding air quality management in the city of Tripoli.
dc.identifier.citationhttp://hdl.handle.net/20.500.14411/1846
dc.identifier.issn2073-4433
dc.identifier.urihttps://doi.org/10.3390/atmos14030478
dc.language.isoen
dc.publisherAtmosphere
dc.relation.ispartofseries14; 3
dc.subjectNeural network modeling, time series analysis, particulate matter, modeling and fore- 25 casting, Libya
dc.titleForecasting Air Quality in Tripoli: An Evaluation of Deep Learning Models for Hourly PM2.5 Surface Mass Concentrations
dc.title.alternativeAn Evaluation of Deep Learning Models for Hourly PM2.5 Surface Mass Concentrations
dc.typeArticle
dspace.entity.typeArticle

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