Tehran Air Quality Index Forecasting
A forecasting study of Tehran’s Air Quality Index built on a decade of historical pollutant measurements. Decision Tree, Support Vector Machine, and Random Forest models were trained and evaluated on the dataset to predict AQI levels, comparing how well each captures the temporal patterns and pollutant interactions that drive air quality in the city.
Mohammadhossein Akbari Moafi
AI Researcher and Engineer passionate about building practical, efficient intelligent systems across vision, multimodal, and knowledge-driven applications.