Application of various machine learning algorithms in view of predicting the CO2 emissions in the transportation sector

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dc.authoridYILBASI, ZEKI/0000-0002-5906-3538en_US
dc.authoridAgbulut, Umit/0000-0002-6635-6494en_US
dc.authoridYESILYURT, Murat Kadir/0000-0003-0870-7564en_US
dc.authorscopusid57215302642en_US
dc.authorscopusid56662071000en_US
dc.authorscopusid57202959651en_US
dc.authorscopusid37068368700en_US
dc.authorscopusid57264764000en_US
dc.authorwosidYILBASI, ZEKI/AFX-2446-2022en_US
dc.contributor.authorCinarer, Goekalp
dc.contributor.authorYesilyurt, Murat Kadir
dc.contributor.authorAgbulut, Uemit
dc.contributor.authorYilbasi, Zeki
dc.contributor.authorKilic, Kazim
dc.date.accessioned2024-08-23T16:03:42Z
dc.date.available2024-08-23T16:03:42Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractThis study applies three different artificial intelligence algorithms (Multi-layer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)) to estimate CO2 emissions in Turkiye's transportation sector. The input parameters considered are Energy consumption (ENERGY), Vehicle Kilometers (VK), POPulation (POP), Year (Y), and Gross Domestic Product Per Capita (GDP). Strong correlations are observed, with ENERGY having the highest correlation followed by VK, POP, Y, and GDP. Four scenarios are designed based on the correlation effect: scenario 1 (ENERGY/VK/POP/Y/GDP), scenario 2 (ENERGY/VK/POP/Y), scenario 3 (ENERGY/VK/POP), and scenario 4 (ENERGY/VK). Experiments compare their effects on CO2 emissions using statistical indicators (R-2, RMSE, MSE, and MAE). Across all scenarios and algorithms, R-2 values range from 0.8969 to 0.9886, and RMSE values range from 0.0333 to 0.1007. The XGBoost algorithm performs best in scenario 4. Artificial intelligence algorithms prove successful in estimating CO2 emissions. This study has significant implications for policymakers and stakeholders. It highlights the need to review energy investments in transportation and implement regulations, restrictions, legislation, and obligations to reduce emissions. Artificial intelligence algorithms offer the potential for developing effective strategies. Policymakers can use these insights to prioritize sustainable energy investments. In conclusion, this study provides insights into the relationship between input parameters and CO2 emissions in the transportation sector. It emphasizes the importance of proactive measures and policies to address the sector's environmental impact. It also contributes to the understanding of AI-assisted CO2 emissions forecasting in the transport sector, potentially informing future policy decisions aimed at emission reduction and sustainable transport development.en_US
dc.identifier.doi10.2516/stet/2024014
dc.identifier.issn2804-7699
dc.identifier.scopus2-s2.0-85188011009en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org/10.2516/stet/2024014
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13873
dc.identifier.volume79en_US
dc.identifier.wosWOS:001185779400004en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherEdp Sciences S Aen_US
dc.relation.ispartofScience And Technology For Energy Transitionen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCO2 emissionsen_US
dc.subjectTransportation sectoren_US
dc.subjectArtificial intelligenceen_US
dc.subjectMachine learning algorithmen_US
dc.subjectStatistical indicatorsen_US
dc.subjectGlobal Solar-Radiationen_US
dc.subjectBiodieselen_US
dc.subjectModelsen_US
dc.subjectSelectionen_US
dc.subjectOilsen_US
dc.titleApplication of various machine learning algorithms in view of predicting the CO2 emissions in the transportation sectoren_US
dc.typeArticleen_US

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