Machine Learning for Urban Air Quality Forecasting: A Comparative Study of Algorithms

Abstract

Using historical air quality data from five metropolitan cities, we compare the predictive accuracy of several machine learning models, including Random Forest, SVM, and LSTM networks, for forecasting PM2.5 and NOx levels.