An Automated PM2.5 Analysis and Prediction System with Encoder-Decoder Architecture and Continual Learning Mechanism

Over recent decades, air pollution, particularly fine particulate matter (PM2.5), has garnered significant attention due to its detrimental effects on human health. As a result, accurately forecasting air quality has become a critical objective to inform and guide policy-making. However, in developing countries, due to their financial constraints, the prediction ability mostly depends on sparsely distributed monitoring devices, which are insufficient. Various prediction approaches utilizing deep learning techniques, such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models, have been developed to address this gap. Despite their promise, these models encounter challenges, including slow preprocessing times and limited adaptability to changing air quality conditions. This paper proposes a simple Encoder-Decoder model for predicting hourly and daily PM2.5 concentrations. In addition, this paper implements an automated system that collects and processes real-time data, continuously updating the model to respond to emerging trends and variations in pollution levels. The model’s performance is evaluated in Vietnam’s two largest cities, Hanoi and Ho Chi Minh City, demonstrating prediction accuracy in various scenarios.