Air Quality Prediction and Analysis using Machine Learning

2025-04-07

This project examines the use of machine learning techniques to predict and analyse air quality. As a result of increasing urbanisation and industrial activities, air pollution is of critical concern. With machine learning algorithms, we can effectively predict air quality, identify the source of pollution and implement interventions in time.

Introduction

• The importance of controlling air quality.

• An overview of machine learning in environmental science.

Collection of data

• Sources of air quality data (e.g. government databases, IoT sensors).

• Data types: meteorological, pollutant levels and traffic data.

Pre-processing

• Data cleaning: handling missing values and outliers.

• Select theme: identify key variables affecting air quality.

Machine learning models

• Controlled learning: regression models (e.g. linear regression, Random Forest).

• Unsupervised learning: clusters for identifying the source of pollution.

Assessment sample

• Methods of performance assessment (e.g. RMSE, MAE).

• Cross-validation techniques to ensure the reliability of the model.

Applications

• Real- time air quality forecast.

• Policy-making and urban planing based on predictive interventions.

Conclusion

• The opportunities for machine learning to improve air quality management.

• The future direction of research and technological integration.