Forecasting ground-level ozone and fine particulate matter concentrations at Craiova city using a meta-hybrid deep learning model

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dc.contributor.authorEl Mghouchi, Youness
dc.contributor.authorUdristioiu, Mihaela T.
dc.contributor.authorYildizhan, Hasan
dc.contributor.authorBrancus, Mihaela
dc.date.accessioned2025-01-06T17:38:19Z
dc.date.available2025-01-06T17:38:19Z
dc.date.issued2024
dc.description.abstractAir quality forecasting is vital for managing and mitigating the adverse effects of air pollution on human health, crops, and the environment. This study aims to forecast daily time series of ozone and fine particulate matter (PM) concentrations using a meta-hybrid deep NARMAX (Nonlinear Auto-Regressive Moving Average with eXogenous inputs) model. Two datasets were utilised: (a) data on meteorological parameters (temperature, air pressure, relative humidity) and air pollutant concentrations (particulate matter, ozone, dioxide of carbon, volatile organic compounds, formaldehyde) provided by a sensor model A3 situated in the centre of Craiova city, and (b) data on wind direction, wind speed, and sunshine duration provided by the National Meteorological Administration. The data sets covered a time interval from December 10, 2020, to January 05, 2024. Initially, a statistical analysis was conducted to assess the correlation between variables. Results revealed that ozone concentration is primarily influenced by meteorological variables such as temperature (r = 0.79), sunshine duration (r = 0.55), and relative humidity (r = -0.48), and secondarily by air pollution indicators including VOC (r = -0.34), PM concentrations (r = -0.34), and CO2 (r = -0.3). In the subsequent stage, thirteen Machine Learning (ML) models were employed in conjunction with an integral feature selection (IFS) method to identify the best combinations of predictor variables for predicting ozone and PMs. Finally, a deep NARMAX model was developed to forecast the next periods of ozone and PMs based on the optimal combinations identified earlier. Results indicated the selection of sixty best models for ozone forecasting and four best models for PMs. The R-2 values surpassed 0.97 for ozone and exceeded 0.8 for PMs, demonstrating the efficacy of the forecasting approach.
dc.description.sponsorshipThat is a statement of declaration by the authors indicating that they do not have any financial or personal relationships that could potentially influence the results or interpretation of their work. This declaration is a common practice in research papers to ensure transparency and credibility of the reported results.
dc.identifier.doi10.1016/j.uclim.2024.102099
dc.identifier.issn2212-0955
dc.identifier.scopus2-s2.0-85201302062
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.uclim.2024.102099
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2529
dc.identifier.volume57
dc.identifier.wosWOS:001298661000001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofUrban Climate
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectGround level ozone
dc.subjectAir Quality
dc.subjectDeep NARMAX model
dc.subjectPredictor variables
dc.subjectIntegral feature selection
dc.subjectPredicting & forecasting
dc.titleForecasting ground-level ozone and fine particulate matter concentrations at Craiova city using a meta-hybrid deep learning model
dc.typeArticle

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