Deep Learning Solutions for Air Quality Assessment and Forecasting

Abstract

The increasing rate of urbanization, industrialization, and car emissions, air quality has emerged as an important environmental and health issue. The issues concerning early warning systems, environmental policies, and environmental and health hazards can be diminished by precise air quality monitoring and prediction. To accurately analyze air quality statistics and generate highly precise forecasts regarding air quality indices (AQI), this paper examines deep learning techniques. We develop and test Deep Learning models like Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and combined models by leveraging past statistics of pollutant concentrations, environmental factors, and trends regarding air quality. In regards to accuracy and robustness, the experimental results reveal that the models and techniques of deep learning are more effective than the conventional statistical and machine learning approaches. These results illustrate the promise of the latest AI methods and techniques introduced to ensure public health and air quality. In particular, when it comes to rapidly urbanizing regions, precise predictions of air quality are equally essential for public health and optimal air pollution management in an effective manner. The study is centered on Deep Learning algorithms which predict air quality (AQ) values of the major pollutants present in the ambient air in particular PM2.5, PM10, NOx, SO2, CO. Regular machine learning methods often fall short in representing complex non-linear relationships in meteorological and environmental datasets. For this purpose, various Deep Learning architectures, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) networks are explored.