Grid Search Optimized Machine Learning based Modeling of CO2 Emissions Prediction from Cars for Sustainable Environment

Abstract : Carbon emissions have increased dramatically because of industrialization, trapping heat in the atmosphere and hastening climate change. This is a serious threat to the wealth, security, and well-being of the world. The effects are extensive, ranging from severe weather, disease outbreaks, and economic disruption to food insecurity and water scarcity. The World Health Organization (WHO) has determined that climate change poses the greatest threat to public health in the twenty-first century. Thus, precise CO2 emissions have emerged as a crucial concern in recent times. Several studies have tried to forecast the amount CO2 from industry and power plant using statistical analysis. Efficiency, robustness and diverse application was the limitation of the study. In this study, we have proposed an AI based model that is able to predict the amounts of CO2 emissions from cars. We applied a grid search-optimized machine learning approach using the publicly available Canadian dataset. Incorporation of different statistical analyses and preprocessing techniques such as duplicate data management, outlier rejection, scaling contributed to enhance the quality of the dataset. Later, grid search techniques were applied to tune the KNN, RF, and SVR models. The approach has enhanced the performance of CO2 emissions prediction. In the study, we further used the explainability of the random forest model to check the bias and fairness of predictability. MSE, RMSE, and R-squared metrics of the proposed approach were the highest as the state of the art.