Spatiotemporal Graph Convolutional Recurrent Neural Network Model for
Citywide Air Pollution Forecasting
- URL: http://arxiv.org/abs/2304.12630v1
- Date: Tue, 25 Apr 2023 07:57:07 GMT
- Title: Spatiotemporal Graph Convolutional Recurrent Neural Network Model for
Citywide Air Pollution Forecasting
- Authors: Van-Duc Le
- Abstract summary: Air pollution varies in a manner and depends on many complicated factors.
An image-based representation may not be ideal as air pollution and other impact factors have natural graph structures.
A Graph Convolutional Network (GCN) can efficiently represent the spatial features of air quality readings in the whole city.
Our approach is superior to a hybrid GCN-based method in a real-world air pollution dataset.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Citywide Air Pollution Forecasting tries to precisely predict the air quality
multiple hours ahead for the entire city. This topic is challenged since air
pollution varies in a spatiotemporal manner and depends on many complicated
factors. Our previous research has solved the problem by considering the whole
city as an image and leveraged a Convolutional Long Short-Term Memory
(ConvLSTM) model to learn the spatiotemporal features. However, an image-based
representation may not be ideal as air pollution and other impact factors have
natural graph structures. In this research, we argue that a Graph Convolutional
Network (GCN) can efficiently represent the spatial features of air quality
readings in the whole city. Specially, we extend the ConvLSTM model to a
Spatiotemporal Graph Convolutional Recurrent Neural Network (Spatiotemporal
GCRNN) model by tightly integrating a GCN architecture into an RNN structure
for efficient learning spatiotemporal characteristics of air quality values and
their influential factors. Our extensive experiments prove the proposed model
has a better performance compare to the state-of-the-art ConvLSTM model for air
pollution predicting while the number of parameters is much smaller. Moreover,
our approach is also superior to a hybrid GCN-based method in a real-world air
pollution dataset.
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