pm2.5-gnn's Introduction

PM2.5-GNN

PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting

Dataset

Download dataset KnowAir from Google Drive or Baiduyun with code ni44.

Requirements

Python 3.7.3
PyTorch 1.7.0
PyG: https://github.com/rusty1s/pytorch_geometric#pytorch-170

pip install -r requirements.txt

Experiment Setup

open config.yaml, do the following setups.

set data path after your server name. Like mine.

filepath:
  GPU-Server:
    knowair_fp: /data/wangshuo/haze/pm25gnn/KnowAir.npy
    results_dir: /data/wangshuo/haze/pm25gnn/results

Uncomment the model you want to run.

#  model: MLP
#  model: LSTM
#  model: GRU
#  model: GC_LSTM
#  model: nodesFC_GRU
   model: PM25_GNN
#  model: PM25_GNN_nosub

Choose the sub-datast number in [1,2,3].

 dataset_num: 3

Set weather variables you wish to use. Following is the default setting in the paper. You can uncomment specific variables. Variables in dataset KnowAir is defined in metero_var.

  metero_use: ['2m_temperature',
               'boundary_layer_height',
               'k_index',
               'relative_humidity+950',
               'surface_pressure',
               'total_precipitation',
               'u_component_of_wind+950',
               'v_component_of_wind+950',]

Run

python train.py

Reference

Shuo Wang, Yanran Li, Jiang Zhang, Qingye Meng, Lingwei Meng, and Fei Gao. 2020. PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting. In 28th International Conference on Advances in Geographic Information Systems (SIGSPATIAL ’20), November 3–6, 2020, Seattle, WA, USA. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3397536.3422208

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