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

• The Random Forest model is applied to predict the CO 2 emission caused by road transport. • Six Meta-Heuristic Algorithms are proposed to optimize the hyperparameters of the Random Forest model. • The hybrid RF-SMA model has the best performance in improving predictions. • The R² metric for the proposed model is 0.9942 for train data and 0.9641 for test data. Carbon dioxide is one of the most important pollutants in urban areas. Since the relationship between the factors of road transport and CO 2 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 CO 2 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 CO 2 emission. [ABSTRACT FROM AUTHOR]