# PM2.5-GNN

**Repository Path**: ying920/PM2.5-GNN

## Basic Information

- **Project Name**: PM2.5-GNN
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-04-10
- **Last Updated**: 2024-04-10

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## 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.

![](https://tva1.sinaimg.cn/large/0081Kckwly1gjy8kojsfmj30i202g746.jpg)

```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}
}
```