Akademik Veri Yönetim Sistemi
PEERJ COMPUTER SCIENCE, cilt.10, 2024 (SCI-Expanded)
Background. . The continuous increase in carbon dioxide (CO2) 2 ) emissions from fuel vehicles generates a greenhouse effect in the atmosphere, which has a negative impact on global warming and climate change and raises serious concerns about environmental sustainability. Therefore, research on estimating and reducing vehicle CO2 2 emissions is crucial in promoting environmental sustainability and reducing greenhouse gas emissions in the atmosphere. Methods. . This study performed a comparative regression analysis using 18 different regression algorithms based on machine learning, ensemble learning, and deep learning paradigms to evaluate and predict CO2 2 emissions from fuel vehicles. The performance of each algorithm was evaluated using metrics including R2 , 2 , Adjusted R2 , 2 , root mean square error (RMSE), and runtime. Results. . The findings revealed that ensemble learning methods have higher prediction accuracy and lower error rates. Ensemble learning algorithms that included Extreme Gradient Boosting (XGB), Random Forest, and Light Gradient-Boosting Machine (LGBM) demonstrated high R2 2 and low RMSE values. As a result, these ensemble learning-based algorithms were discovered to be the most effective methods of predicting CO2 2 emissions. Although deep learning models with complex structures, such as the convolutional neural network (CNN), deep neural network (DNN) and gated recurrent unit (GRU), achieved high R2 2 values, it was discovered that they take longer to train and require more computational resources. The methodology and findings of our research provide a number of important implications for the different stakeholders striving for environmental sustainability and an ecological world.