Predicting CO2 Emissions by Vehicles Using Machine Learning

Abstract

In the previous decade, the measure of CO2 outflows has seen a critical increment, this is coming about to be a significant justification for environmental change. A significant supporter of these emanations is the transport sector. This sector alone is assessed to represent about 16.2% of the absolute worldwide CO2 outflows. The focal point of this paper is to examine and audit diverse AI and ML techniques that can be utilized to decrease these CO2 outflows caused because of the transport sector. The paper talks about an ML approach to deal with anticipated CO2 discharges brought about by vehicles. Utilizing the aftereffects of this model, the neighborhood overseeing bodies can get ready for a superior public vehicle foundation if necessary. Region savvy examination can assist the overseeing bodies with managing the progression of the public vehicles in various locales of the city which will, thus, bring about a decrease of CO2 discharges. The model has shown promising results by achieving an RMSLE of 0.71 and accuracy of about 99.8% using Machine Learning models like Lasso Regression, Multiple Linear Regression, XGBoost, Support Vector Regressor (SVR), Random Forest, and Ridge Regression.