Prediction, modelling, and forecasting of PM and AQI using hybrid machine learning

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dc.authoridEl Mghouchi, Youness/0000-0003-2164-2056
dc.contributor.authorUdristioiu, Mihaela T.
dc.contributor.authorEL Mghouchi, Youness
dc.contributor.authorYildizhan, Hasan
dc.date.accessioned2025-01-06T17:36:11Z
dc.date.available2025-01-06T17:36:11Z
dc.date.issued2023
dc.description.abstractThis paper proposes a combination of hybrid models like Input Variable Selection (IVS), Machine Learning (ML), and regression method to predict, model, and forecast the daily concentrations of particulate matter (PM1, PM2.5, PM10) and Air Quality Index (AQI). A sensor placed in the centre of Craiova, Romania, provides a two-year dataset for training, testing, and validation phases. The analysis identifies the most important predictor variables for PM prediction and forecasting. The coefficient of determination (R2) values in this stage exceeded 0.95 (95%), indicating a strong correlation between PM concentrations. The performance of the proposed models is evaluated by objective measures, including root mean squared error (RMSE) and standard deviation (a). RMSE ranged between 0.65 and 1 & mu;g/m3, while a has values between 2.75 and 4.1 & mu;g/m3, reflecting a high level of precision and a successful performance of the proposed models. Furthermore, 13 multivariable-based PM models are developed in this study and adjusted using a hybrid Least Square -Decision Tree approach. The R2 values for these adjusted models range from 0.66 to 0.75, while the RMSE and a vary between 8 and 9.1 & mu;g/m3. Finally, a handled application for multistep-ahead time series forecasting is elaborated by combining the Nonlinear System Identification (NARMAX) approach with Decision Tree machine learning. This application allows for forecasting PM concentrations and AQI for the next periods. The R2 values obtained in this stage surpass 0.93, indicating almost a high level of accuracy. The RMSE ranged between 4.43 and 6.25 & mu;g/m3, while the a ranged between 4.44 and 6.26 & mu;g/m3, further validating the precision of our forecasting model.
dc.identifier.doi10.1016/j.jclepro.2023.138496
dc.identifier.issn0959-6526
dc.identifier.issn1879-1786
dc.identifier.scopus2-s2.0-85168153694
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.jclepro.2023.138496
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1797
dc.identifier.volume421
dc.identifier.wosWOS:001061552600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofJournal of Cleaner Production
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectAir quality
dc.subjectPM concentrations
dc.subjectHybrid machine learning
dc.subjectPM forecasting
dc.subjectPM sensor
dc.subjectCraiova
dc.titlePrediction, modelling, and forecasting of PM and AQI using hybrid machine learning
dc.typeArticle

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