Broad-UNet: Multi-scale feature learning for nowcasting tasks

The official code of the following paper: https://www.sciencedirect.com/science/article/pii/S089360802100349X

Results

Some results for the different nowcasting tasks

Task	Actual Vs Prediction
Precipitation prediction (30 mins ahead)
Precipitation prediction (30 mins ahead)
Cloud cover prediction (30 mins ahead)
Cloud cover prediction (90 mins ahead)

Installation

The required modules can be installed via:

pip install -r requirements.txt

Quick Start

Depending on the nowcasting task to be performed, the models can be trained running:

python training_clouds_data.py

or

python training_precipitation_data.py

To evaluate the models and visualize some predictions, please run:

python evaluation_and_predictions_clouds.py

or

python evaluation_and_predictions_precipitation.py

Scripts

• The scripts contain the models, the generators, the training files and evaluation files.

Broad-UNet architecture

Data and pretrained models

In order to download the data or any of the trained models, please email to the following address:

siamak.mehrkanoon@maastrichtuniversity.nl

The data must be downloaded and unzipped inside the 'dataset_clouds/' or 'dataset_precipitation' directories as indicated in the txt files inside them.

Citation

If you use our data and code, please cite the paper using the following bibtex reference:

@article{ Broad-UNet,
      title={Broad-UNet: Multi-scale feature learning for nowcasting tasks},
      author={Fernandez, Jesus Garcia and Mehrkanoon, Siamak},
      journal={Neural Networks},
      volume={144},
      pages={419--427},
      year={2021},
      publisher={Elsevier}
}