Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/shuowang-ai/PM2.5-GNN
PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting
https://github.com/shuowang-ai/PM2.5-GNN
Last synced: about 1 month ago
JSON representation
PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting
- Host: GitHub
- URL: https://github.com/shuowang-ai/PM2.5-GNN
- Owner: shuowang-ai
- License: mit
- Created: 2020-10-11T12:04:23.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2023-03-07T14:35:46.000Z (over 1 year ago)
- Last Synced: 2024-06-11T20:34:38.479Z (3 months ago)
- Language: Python
- Size: 9.14 MB
- Stars: 152
- Watchers: 4
- Forks: 55
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- open-sustainable-technology - PM2.5-GNN - A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting. (Natural Resources / Air Quality)
README
# PM2.5-GNN
PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting
## Dataset
- Download dataset **KnowAir** from [Google Drive](https://drive.google.com/open?id=1R6hS5VAgjJQ_wu8i5qoLjIxY0BG7RD1L) or [Baiduyun](https://pan.baidu.com/s/18D6Etl5Lm1E4vOLVrX0ZAw) with code `t82d`.
## Requirements
```
Python 3.7.3
PyTorch 1.7.0
PyG: https://github.com/rusty1s/pytorch_geometric#pytorch-170
```
```bash
pip install -r requirements.txt
```
## Experiment Setup
open `config.yaml`, do the following setups.
- set data path after your server name. Like mine.
```python
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.
```python
# 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].
```python
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`.
```python
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
```bash
python train.py
```
## Reference
Paper: https://dl.acm.org/doi/10.1145/3397536.3422208
```
@inproceedings{10.1145/3397536.3422208,
author = {Wang, Shuo and Li, Yanran and Zhang, Jiang and Meng, Qingye and Meng, Lingwei and Gao, Fei},
title = {PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting},
year = {2020},
isbn = {9781450380195},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3397536.3422208},
doi = {10.1145/3397536.3422208},
abstract = {When predicting PM2.5 concentrations, it is necessary to consider complex information sources since the concentrations are influenced by various factors within a long period. In this paper, we identify a set of critical domain knowledge for PM2.5 forecasting and develop a novel graph based model, PM2.5-GNN, being capable of capturing long-term dependencies. On a real-world dataset, we validate the effectiveness of the proposed model and examine its abilities of capturing both fine-grained and long-term influences in PM2.5 process. The proposed PM2.5-GNN has also been deployed online to provide free forecasting service.},
booktitle = {Proceedings of the 28th International Conference on Advances in Geographic Information Systems},
pages = {163–166},
numpages = {4},
keywords = {air quality prediction, graph neural network, spatio-temporal prediction},
location = {Seattle, WA, USA},
series = {SIGSPATIAL '20}
}
```