Imbalanced Regression Model of Auroral Electrojet Indices: Can We Predict Super Substorms?

This Zenodo database supports the Imbalanced Regression Artificial Neural Network model for Substorms (IRANN-S), which applies weighting to different SuperMag AL (SML) index values.

Contact

Name: Xiangning Chu Email: chuxiangning@gmail.com

Paper Title

Imbalanced Regression Model of Auroral Electrojet Indices: Can We Predict Super Substorms?

Authors & Affiliations

1. Xiangning Chu – Laboratory for Atmospheric and Space Physics, University of Colorado Boulder, Boulder, CO, USA

2. Lucas Jia – Department of Electrical & Computer Engineering, University of California Santa Barbara, Santa Barbara, CA, USA

3. Robert L. McPherron – Department of Earth, Planetary, and Space Sciences, University of California, Los Angeles, CA, USA

4. Xinlin Li – Department of Aerospace Engineering Sciences, University of Colorado Boulder, Boulder, CO, USA

5. Jacob Bortnik – Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA, USA

Rules of the Road

The code and models are publicly distributed by the authors. To ensure continued research activity, users should contact the authors if these models and datasets are used in any publications, presentations, or any other formats.

• Authorship: If these products are used in a publication, please contact the authors and provide proper attribution.

• Usage: Data, plots, and derived products are provided under fair use limitations and can be redistributed.

Repository Description

This repository contains the Python code and model files used to develop the IRANN-S model.

• The models/ folder contains the trained model coefficients in H5 format, which can be loaded using TensorFlow.

• An example Python script (example.py) is provided to demonstrate model usage.

Support

For any questions, please feel free to contact the authors.