Air pollution

Air pollution

Machine Learning Techniques Improve Predictive Models for Emissions in Gasoline-Alcohol Blends

Scientists at Sichuan University have employed machine learning techniques to develop more accurate predictive models for emissions in gasoline-alcohol blends. By using Gradient Boosting, Random Forest, Bootstrap Aggregating, and Extreme Gradient Boosting (XGB) algorithms, the researchers aimed to improve the accuracy of predictions for carbon monoxide, unburned hydrocarbons, and nitrogen