Deep-AIR: A Hybrid CNN-LSTM Framework forFine-Grained Air Pollution Forecast

Deep-AIR: A Hybrid CNN-LSTM Framework forFine-Grained Air Pollution Forecast
June 16, 2022
Research

Poor air quality has become an increasingly critical challenge for many metropolitan cities, which carries many catastrophic physical and mental consequences on human health and quality of life. However, accurately monitoring and forecasting air quality remains a highly challenging endeavour. Limited by geographically sparse data, traditional statistical models and newly emerging data-driven methods of air quality forecasting mainly focused on the temporal correlation between the historical temporal datasets of air pollutants. However, in reality, both distribution and dispersion of air pollutants are highly location-dependant. In this paper, we propose a novel hybrid deep learning model that combines Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) together to forecast air quality at high-resolution. Our model can utilize the spatial correlation characteristic of our air pollutant data sets to achieve higher forecasting accuracy than existing deep learning models of air pollution forecast.