An Effective Prediction of Air Pollution by Using Machine Learning Models

Abstract

Air pollution is a global issue posing health risks and environmental degradation. Governments in developing countries, particularly India, are tasked with regulating air pollutant levels. IoT-based air quality monitoring systems collect data on pollutants like PM2.5, NO2, SO2, ozone, and CO. The project aims to create a hybrid machine learning algorithm that synergizes the advantages of both Random Forest and Linear Regression models to provide precise predictions of air pollution levels. A user-friendly website will be developed to disseminate real-time air quality alerts to the public. The project focuses on enhancing the machine learning model, optimizing location-based recommendations, implementing user authentication, integrating a database for data storage, refining push notifications, fostering community engagement, and ensuring robust privacy measures. The objective of this project is to improve the accuracy and timeliness of predictions related to air pollution, providing decision-makers with actionable insights to effectively address concerns about air quality.