Deep Learning and IoT based Air Quality Monitoring and Prediction System

The air quality is quite poor due to the high quantities of particulate matter in the surrounding environment. Incomplete coal combustion, automobile emissions, and particulate matter (PM2.5) emissions are all potential causes of air pollution that may have an impact on human health. Carbon Monoxide (CO) is another gas produced mostly by incomplete combustion of fuel. Vehicle engines are responsible for 80% of carbon monoxide emissions in urban areas. Smog is an inescapable component of city life, especially in big metropolitan areas. This paper introduces an IoT and deep learning-based architecture for air quality monitoring and forecasting. In this framework, the input data set is created by gathering air quality-related data from various sensors and IoT devices. The raw input data set is then reduced into features using the principal component analysis approach. This strategy is also useful for coping with noisy and missing data in the data collection. The classification model is created utilising convolution, neural network, recurrent neural network, and LSTM deep learning approaches. The classification model is trained and tested on a preprocessed data set. Performance of various models is evaluated using metrics such as R2, RMSE, and MSE. The air quality index is also anticipated in the conclusion. Major Findings: The finding shows that the CNN consistently outperforms the other models, achieving the lowest MSE and RMSE values and the highest R² scores. This implies that CNN is the most effective model for predicting air pollution levels.