Prediction of daily fine particulate matter (PM2.5) concentration in Aksaray, Turkey: Temporal variation, meteorological dependence, and employing artificial neural network

dc.authorid0000-0002-6419-2318
dc.contributor.authorKoçak, Ebru
dc.date.accessioned2024-07-11T06:47:03Z
dc.date.available2024-07-11T06:47:03Z
dc.date.issued2024
dc.departmentMühendislik Fakültesi
dc.description.abstractThis study analyzed the temporal variation and prediction of fine particulate matter (PM2.5) concentrations in Aksaray, Turkey, a city in Central Anatolia. The relationship between PM2.5 and meteorological parameters such as temperature, humidity, wind speed, and wind direction was investigated. An artificial neural network (ANN) model was developed to predict PM2.5 levels based on meteorological data and air pollutant information. Seasonal and diurnal patterns of PM2.5 concentrations were observed, with higher values recorded during the winter and lower values during the summer. Additionally, higher levels were observed in the morning and evening, while lower levels were recorded in the afternoon. The variations in meteorological parameters, especially temperature and wind speed, significantly influenced PM2.5 levels. To predict hourly PM2.5 concentrations, single and multiple data imputation techniques were employed in combination with resilient back-propagation (RPROP-ANN). The neural network was applied, consisting of one input layer comprising 11 parameters, one hidden layer with 20 neurons, and an output layer. The results indicate that the best forecasting performance for PM2.5 was demonstrated by the combination of the missForest imputation technique with the RPROP neural network, as assessed by the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The proposed model is characterized by a low RMSE of 5.94 and a high R2 value of 0.88, demonstrating exceptional predictive performance in air quality.
dc.identifier.doi10.1002/ep.14355
dc.identifier.issn1944-7442
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ2
dc.identifier.urihttps:/dx.doi.org/10.1002/ep.14355
dc.identifier.urihttps://hdl.handle.net/20.500.12451/12106
dc.identifier.volume43en_US
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherJohn Wiley and Sons Inc
dc.relation.ispartofEnvironmental Progress and Sustainable Energy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectArtificial Neural Network
dc.subjectMeteorological Parameters
dc.subjectRandom Forest Imputation
dc.subjectTemporal Variation
dc.titlePrediction of daily fine particulate matter (PM2.5) concentration in Aksaray, Turkey: Temporal variation, meteorological dependence, and employing artificial neural network
dc.typeArticle

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