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MSTGAN is an innovative method designed for multi-station urban air quality prediction, which fully considers the individual, global, and local multi-scale information of air quality spatiotemporal sequences. It incorporates an attention-based dynamic graph modeling approach to capture global spatiotemporal dependencies.

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MSTGAN

Predicting air quality with a multi-scale spatio-temporal graph attention network

This repo is the implementation of our manuscript entitled Predicting air quality with a multi-scale spatio-temporal graph attention network. The code is based on Pytorch 1.12.1, and tested on a GeForce RTX 4090 GPU with 24GB memory.

In this study, we present an graph-attention-based approach for air quality prediction at multiple monitoring stations termed the Multi-scale Spatio-Temporal Graph Attention Network (MSTGAN). Experiments with two real-world datasets showed the proposed MSTGAN achieved the highest prediction accuracies for 12,18 and 24-hour prediction time lengths, compared to several state-of-the-art methods.

Framework

MSTGAN

Requirements

MSTGAN uses the following dependencies

  • Pytorch 1.12.1 and its dependencies
  • Numpy and Pandas
  • CUDA 11.8 or latest version

Folder Structure

We list the code of the major modules as follows:

Arguments

We introduce some major arguments of our main function here.

Training settings:

  • train_rate: rate of train set
  • test_rate: rate pf test set
  • lag: time length of hidtorical steps
  • pre_len: time length of future steps
  • num_nodes: the number of stations
  • batch_size: training or testing batch size
  • input_dim: the feature dimension of inputs
  • output_dim: the feature dimension of outputs
  • learning_rate: the learning rate at the beginning
  • epochs: training epochs
  • early_stop_patience: the patience of early stopping
  • device: using which GPU to train our model
  • seed: the random seed for experiments

Model hyperparameters:

  • d_model: position encoding embedding dimension
  • cheb_k: Chebyshev polynomials order
  • block1_hidden: number of hidden layers in the first block
  • block2_hidden: number of hidden layers in the second block
  • time_strides: time resolution
  • nb_block: number of Multi-Spatio-Temporal_Block (MST_Block)
  • dropout: dropout rate

Citation

  • If you find our work useful in your research, please cite:
    Zhou, X., Wang, J., Wang, J. & Guan, Q.* (2024) Predicting air quality using a multi-scale spatiotemporal graph attention network, Information Sciences, 680: 121072. DOI: 10.1016/j.ins.2024.121072

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MSTGAN is an innovative method designed for multi-station urban air quality prediction, which fully considers the individual, global, and local multi-scale information of air quality spatiotemporal sequences. It incorporates an attention-based dynamic graph modeling approach to capture global spatiotemporal dependencies.

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