Multi-Group Encoder-Decoder Networks to Fuse Heterogeneous Data for
Next-Day Air Quality Prediction
Abstract
Accurate next-day air quality prediction is essential
to enable warning and prevention measures for cities
and individuals to cope with potential air pollution,
such as vehicle restriction, factory shutdown, and
limiting outdoor activities. The problem is challenging because air quality is affected by a diverse set
of complex factors. There has been prior work on
short-term (e.g., next 6 hours) prediction, however,
there is limited research on modeling local weather
influences or fusing heterogeneous data for next-day
air quality prediction. This paper tackles this problem through three key contributions: (1) we leverage
multi-source data, especially high-frequency gridbased weather data, to model air pollutant dynamics
at station-level; (2) we add convolution operators on
grid weather data to capture the impacts of various
weather parameters on air pollutant variations; and
(3) we automatically group (cross-domain) features
based on their correlations, and propose multi-group
Encoder-Decoder networks (MGED-Net) to effectively fuse multiple feature groups for next-day air
quality prediction. The experiments with real-world
data demonstrate the improved prediction performance of MGED-Net over state-of-the-art solutions
(4.2 % to 9.6 % improvement in MAE and 9.2 % to
16.4 % improvement in RMSE)