AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction

This repo is the Pytorch implementation of our manuscript titled AirPhyNet: Physics-Guided Neural Networks for Air Quality Prediction. In this study, we present a novel physics guided differential equation network for precise air quality prediction over the next 72 hours with a physical meaning. The foundational training framework for this project is derived from Echo-Ji.

Framework

Requirement

• scipy>=1.5.2
• numpy>=1.19.1
• pandas>=1.1.5
• pyyaml>=5.3.1
• pytorch>=1.7.1
• future>=0.18.2
• torchdiffeq>=0.2.0

Dependency can be installed using the following command:

pip install -r requirements.txt

Data Preparation

The sample air quality data files for Beijing area are available at Google Drive and should be put into the data/ folder for running the code.

Model Traning and Evaluation

The following code can be used to train and evaluate the model.

python main.py --config_filename = configs.yaml

Citation

If you find our work useful in your research, please cite:

@article{hettige2024airphynet,
  title={AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction},
  author={Hettige, Kethmi Hirushini and Ji, Jiahao and Xiang, Shili and Long, Cheng and Cong, Gao and Wang, Jingyuan},
  journal={arXiv preprint arXiv:2402.03784},
  year={2024}
}