Abstract:
Accurate air quality forecasting is essential in managing outdoor activity risk and responding to pollution emergencies. However, effectively modeling complex underlying ...Show MoreMetadata
Abstract:
Accurate air quality forecasting is essential in managing outdoor activity risk and responding to pollution emergencies. However, effectively modeling complex underlying spatiotemporal dependencies among monitoring stations remains a challenging task. Most existing methods deeply rely on local features to model dynamic spatial correlations and on RNNs to model temporal evolution. In this paper, we propose a novel multi-task deep spatiotemporal graph neural network, named MTGnet, for air quality prediction. MTGnet's main advantage is its ability to adaptively capture complex correlations among different stations, represented as an adjacency matrix, using both local features and global patterns. MTGnet consists of multiple convolutional layers for aggregating information about nearby stations and extracting essential spatial and temporal features for future air quality prediction. Using this architecture, we implement a multi-task learning scheme that trains the model to predict air quality both finely, at the station level, and coarsely, at the city level. Experiments on multiple real datasets demonstrate that MTGnet outperforms state-of-the-art methods.
Date of Conference: 18-23 July 2022
Date Added to IEEE Xplore: 30 September 2022
ISBN Information: