Estimation of CO<sub>2</sub> Emissions in Transportation Systems Using Artificial Neural Networks, Machine Learning, and Deep Learning: A Comprehensive Approach
This study focuses on estimating transportation system-related emissions in CO<sub>2</sub> eq., considering several socioeconomic and energy- and transportation-related input variables. The proposed approach incorporates artificial neural networks, machine learning, and deep learning alg...
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MDPI AG
2025-03-01
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| Series: | Systems |
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| Online Access: | https://www.mdpi.com/2079-8954/13/3/194 |
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| author | Seval Ene Yalçın |
| author_facet | Seval Ene Yalçın |
| author_sort | Seval Ene Yalçın |
| collection | DOAJ |
| description | This study focuses on estimating transportation system-related emissions in CO<sub>2</sub> eq., considering several socioeconomic and energy- and transportation-related input variables. The proposed approach incorporates artificial neural networks, machine learning, and deep learning algorithms. The case of Turkey was considered as an example. Model performance was evaluated using a dataset of Turkey, and future projections were made based on scenario analysis compatible with Turkey’s climate change mitigation strategies. This study also adopted a transportation type-based analysis, exploring the role of Turkey’s road, air, marine, and rail transportation systems. The findings of this study indicate that the aforementioned models can be effectively implemented to predict transport emissions, concluding that they have valuable and practical applications in this field. |
| format | Article |
| id | doaj-art-b2ebf14dce8541a18854e3c9c694c26e |
| institution | DOAJ |
| issn | 2079-8954 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj-art-b2ebf14dce8541a18854e3c9c694c26e2025-08-20T02:43:03ZengMDPI AGSystems2079-89542025-03-0113319410.3390/systems13030194Estimation of CO<sub>2</sub> Emissions in Transportation Systems Using Artificial Neural Networks, Machine Learning, and Deep Learning: A Comprehensive ApproachSeval Ene Yalçın0Department of Industrial Engineering, Bursa Uludağ University, Görükle Campus, 16059 Bursa, TürkiyeThis study focuses on estimating transportation system-related emissions in CO<sub>2</sub> eq., considering several socioeconomic and energy- and transportation-related input variables. The proposed approach incorporates artificial neural networks, machine learning, and deep learning algorithms. The case of Turkey was considered as an example. Model performance was evaluated using a dataset of Turkey, and future projections were made based on scenario analysis compatible with Turkey’s climate change mitigation strategies. This study also adopted a transportation type-based analysis, exploring the role of Turkey’s road, air, marine, and rail transportation systems. The findings of this study indicate that the aforementioned models can be effectively implemented to predict transport emissions, concluding that they have valuable and practical applications in this field.https://www.mdpi.com/2079-8954/13/3/194artificial neural networksdeep learningmachine learningCO<sub>2</sub> emissionstransport systemsforecasting |
| spellingShingle | Seval Ene Yalçın Estimation of CO<sub>2</sub> Emissions in Transportation Systems Using Artificial Neural Networks, Machine Learning, and Deep Learning: A Comprehensive Approach Systems artificial neural networks deep learning machine learning CO<sub>2</sub> emissions transport systems forecasting |
| title | Estimation of CO<sub>2</sub> Emissions in Transportation Systems Using Artificial Neural Networks, Machine Learning, and Deep Learning: A Comprehensive Approach |
| title_full | Estimation of CO<sub>2</sub> Emissions in Transportation Systems Using Artificial Neural Networks, Machine Learning, and Deep Learning: A Comprehensive Approach |
| title_fullStr | Estimation of CO<sub>2</sub> Emissions in Transportation Systems Using Artificial Neural Networks, Machine Learning, and Deep Learning: A Comprehensive Approach |
| title_full_unstemmed | Estimation of CO<sub>2</sub> Emissions in Transportation Systems Using Artificial Neural Networks, Machine Learning, and Deep Learning: A Comprehensive Approach |
| title_short | Estimation of CO<sub>2</sub> Emissions in Transportation Systems Using Artificial Neural Networks, Machine Learning, and Deep Learning: A Comprehensive Approach |
| title_sort | estimation of co sub 2 sub emissions in transportation systems using artificial neural networks machine learning and deep learning a comprehensive approach |
| topic | artificial neural networks deep learning machine learning CO<sub>2</sub> emissions transport systems forecasting |
| url | https://www.mdpi.com/2079-8954/13/3/194 |
| work_keys_str_mv | AT sevaleneyalcın estimationofcosub2subemissionsintransportationsystemsusingartificialneuralnetworksmachinelearninganddeeplearningacomprehensiveapproach |