AirRadar

This repo is the implementation of our manuscript entitled AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks. The code is based on Pytorch 2.1.0, and tested on Ubuntu 16.04 with a NVIDIA GeForce RTX 2080Ti GPU with 12 GB memory.

In this study, we present a novel deep network named AirRadar to collectively infer nationwide air quality in China.

Requirements

AirFormer uses the following dependencies:

• [Pytorch 2.1.0] and its dependencies
• Numpy and Scipy
• CUDA 12.2, cuDNN.

Folder Structure

We list the code of the major modules as follows:

• The main function to train/test our model: click here.
• The source code of our model: click here.
• The trainer/tester: click here/click here/click here
• Data preparation and preprocessing are located at click here.
• Metric computations: click here.

Arguments

We introduce some major arguments of our main function here.

Training settings:

• mode: indicating the mode, e.g., training or test
• gpu: using which GPU to train our model
• seed: the random seed for experiments
• dataset: which dataset to run
• base_lr: the learning rate at the beginning
• lr_decay_ratio: the ratio of learning rate decay
• batch_size: training or testing batch size
• mask_rate: the mask rate of all nodes
• horizon: the length of future steps
• input_dim: the dimension of inputs
• max_epochs: the maximum of training epochs
• patience: the patience of early stopping
• save_preds: whether to save prediction results

Model hyperparameters:

• n_hidden: hidden dimensions in CT-MSA and DS-MSA
• dropout: dropout rate
• dartboard: which dartboard partition to use. 0: 50-200, 1: 50-200-500, 2: 50, 3: 25-100-250.
• context_num: The number of context.
• block_num : The numbef of Distance-Aware Integrator blocks

Model Training

Before running our code, please add the path of this repo to PYTHONPATH.

export PYTHONPATH=$PYTHONPATH:"the path of this repo"

The following examples are conducted on the tiny dataset:

• Example (AirRadar with default setting):

python ./experiments/airRadar/main.py --mode train --gpu 0 --dataset AIR_TINY

Model Test

To test above trained models, you can use the following command to run our code:

• Example (AirRadar with default setting):

python ./experiments/airRadar/main.py --mode test --gpu 0 --dataset AIR_TINY