Effective Modeling of CO 2 Emissions for Light-Duty Vehicles: Linear and Non-Linear Models with Feature Selection
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- Djeundje, Viani Biatat & Crook, Jonathan, 2019. "Identifying hidden patterns in credit risk survival data using Generalised Additive Models," European Journal of Operational Research, Elsevier, vol. 277(1), pages 366-376.
- Hang Thi Thanh Vu & Jeonghan Ko, 2023. "Inventory Transshipment Considering Greenhouse Gas Emissions for Sustainable Cross-Filling in Cold Supply Chains," Sustainability, MDPI, vol. 15(9), pages 1-22, April.
- Bielaczyc, Piotr & Woodburn, Joseph & Szczotka, Andrzej, 2014. "An assessment of regulated emissions and CO2 emissions from a European light-duty CNG-fueled vehicle in the context of Euro 6 emissions regulations," Applied Energy, Elsevier, vol. 117(C), pages 134-141.
- Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
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Keywords
CO 2 emission; fuel consumption; predictive modeling; linear regression; non-linear; generalized additive models; sustainability;All these keywords.
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