Comparative Analysis of Machine Learning for Predicting Air Quality in Smart Cities

Ambient air pollution is the most harmful environmental risk to health. As urban air quality improves, health costs from air pollution-related diseases diminish. This is why air pollution is a major challenge for the public and government around the world. Deployment of the Internet of Things-based sensors has considerably changed the dynamics of predicting air quality. Air pollution can be predicted using machine learning algorithms Data-based sensors in the context of smart cities. In this paper, we performed pollution forecasting using machine learning techniques while presenting a comparative study to determine the best model to accurately predict air quality. Random Forest is an efficient algorithm capable of detecting air quality.

• air quality forecasting
• smart cities
• IoT sensors
• random forest
• classifier comparison
• urban air pollution

• 11 — Villes et communautés durables

• Air Quality Monitoring and Forecasting
• Air Quality and Health Impacts
• Traffic Prediction and Management Techniques
• air quality forecasting
• smart cities
• IoT sensors
• random forest
• classifier comparison
• urban air pollution
• Environmental Engineering
• Health, Toxicology and Mutagenesis
• Building and Construction
• Environmental Science
• Engineering