Air Quality Index Prediction Project Guide

Air pollution is a critical environmental issue affecting human health and climate change. Monitoring air quality levels in real-time allows authorities to issue warnings and take corrective actions. However, traditional monitoring stations are limited and often reactive rather than predictive. By using machine learning models trained on historical pollution data, weather patterns, and traffic data, we can build predictive systems that forecast air quality before conditions deteriorate, enabling proactive public health measures.

By analyzing air pollutant concentrations such as PM2.5, PM10, NO2, CO, and weather parameters like wind speed and humidity, machine learning models can predict future AQI values. Regression algorithms like Random Forest Regression, XGBoost Regression, and LSTM time series models are highly effective for this task. Building an AQI prediction system provides experience in environmental data analysis, multivariate time series forecasting, and creating socially impactful machine learning applications.

The system collects real-time and historical air pollution and weather data from public APIs or sensors. It preprocesses the data, normalizes pollutant concentrations, and applies regression models to predict AQI values for the upcoming hours or days. Time series forecasting techniques like LSTM models can also be used for longer-range predictions. This allows authorities and citizens to take timely actions like restricting traffic, issuing health advisories, or using air purifiers when needed.

• Collect datasets containing air quality measurements and meteorological parameters over time.
• Preprocess data: handle missing values, normalize pollutant concentrations, engineer time-based features.
• Train regression models like Random Forest Regressor, XGBoost, or build LSTM networks for time series prediction.
• Evaluate using metrics like RMSE (Root Mean Squared Error) and MAE (Mean Absolute Error).
• Deploy a dashboard displaying current and predicted AQI levels for different city locations.

• OpenAQ Air Quality API
• Air Quality Index (AQI) Standards
• TensorFlow Time Series Forecasting