Predicting the carbon dioxide emission caused by road transport using a Random Forest (RF) model combined by Meta-Heuristic Algorithms

Abstract

Carbon dioxide is one of the most important pollutants in urban areas. Since the relationship between the factors of road transport and CO2 emission is often complex, using methods based on computational intelligence can be useful. In this way, a hybrid Random Forest, support vector regression, and response surface methodology are implemented to predict CO2 emission in 30 major cities in China. Also, seven optimizers are applied to the Random Forest, and two optimizers are applied to the support vector regression methods to tune their hyper-parameters. The mentioned methods' accuracy is compared through the Standard Error (SE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Relative Absolute Error (RAE), and the coefficient of determination (R2) statistical indexes. The obtained results reveal that the support vector regression with Harris Hawk optimizer has the best accuracy in the training process with an R2 value of 0.9999, and the Random Forest with the Slime Mould Algorithm with an R2 value of 0.9641 has the best accuracy in the testing process. Hence, the Random Forest with Slime Mould Algorithm (RF-SMA) is the best method to predict CO2 emission.