Detection and Prediction of Air Quality Using Machine Learning

Abstract

This paper presents a novel approach for detecting and predicting air quality using machine learning techniques. The proposed system employs various sensors to collect real-time air quality data, which is then preprocessed and used to train a machine learning model. The model is capable of detecting and predicting air quality parameters, such as particulate matter, nitrogen dioxide, and ozone, with high accuracy. The results demonstrate that the proposed system can provide timely and accurate air quality information, which can be used to inform public health decisions and policy-making. Additionally, the system can help identify the sources of air pollution and develop effective mitigation strategies to improve air quality.