PM2.5 concentration prediction using Generative adversarial network: A novel approach

Abstract

Over the last few years, air pollution has become a matter of great concern. Numerous machine learning and deep learning techniques have been applied to predict PM2.5 (Particulate Matter2.5). However, deterministic models perform forecasting based on the mean of probable outputs and cannot handle the uncertainties in real-life situations. With the aim of solving the low accuracy of PM2.5 concentration prediction during uncertainties, the present study proposed an innovative probabilistic model-Prob PM2.5 which predicts one day ahead PM2.5 concentration for time series data, which is multivariate in nature. First, a comprehensive correlation analysis between the meteorological features and PM2.5 concentration is done. Finally, the Conditional GAN framework is used to train the ProbPM2.5 with the help of adversarial training. The proposed framework that transformed a deterministic model into a probabilistic model provided improved performance. Comparative analysis with conventional models, such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) reveals that ProbPM2.5 outperforms during testing, showcasing resilience in the face of unforeseen events like COVID-19. Hence, the proposed method could perform improved characterization of time series characteristics of the air pollutant changes in order to obtain better accuracy of PM2.5 concentration prediction