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license: cc-by-4.0
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# IonoBench Models


[![GitHub](https://img.shields.io/badge/GitHub-Mert--chan%2FIonoBench-blue?logo=github)](https://github.com/Mert-chan/IonoBench)
[![Paper](https://img.shields.io/badge/Paper-Remote%20Sensing-blue?logo=readthedocs)](https://doi.org/10.3390/rs17152557)
[![HF Datasets](https://img.shields.io/badge/HF%20Datasets-IonoBench-blue?logo=huggingface&logoColor=white)](https://huggingface.co/datasets/Mertjhan/IonoBench)

**IonoBench**: Evaluating Spatiotemporal Models for Ionospheric Forecasting under Solar-Balanced and Storm-Aware Conditions  
*Published in Remote Sensing (MDPI)*  


## Contents

Each model folder contains:

- **Best Checkpoint**  
  Format: `MODELNAME_SessionNAME_best_checkpoint_yyyymmdd_hhmm.pth`  
  Example: `SimVP_AllFeatures/model_best.pth`

- **Training Logs**  
  Format: `MODELNAME_SessionNAME_lrXX_bsXX_yyyymmdd_hhmm.txt`  
  Example: `SimVP_AllFeatures/training_log.txt`

- **Test Results**  
  Format: `testing_info_yyyy-mm-dd_hh-mm.txt`  
  Contains evaluation metrics such as RMSE, R², and SSIM on test and storm periods

## Notes

- Original configuration files are included and reflect the training settings used.  
- A layered configuration structure (`base → model → mode → CLI`) was adopted later for improved usability.
- The pretrained models are intended for reproducibility and evaluation; training tutorials and CLI tools are available on the GitHub page.

## Citation

If you use these models, please cite:

> Mert C. Turkmen, Yee Hui Lee, Eng Leong Tan (2025).  
> *IonoBench: Evaluating Spatiotemporal Models for Ionospheric Forecasting under Solar-Balanced and Storm-Aware Conditions*.  
> Remote Sensing, 17(15), 2557. [https://doi.org/10.3390/rs17152557](https://doi.org/10.3390/rs17152557)

As well as the **original refences**:

- **SimVPv2**: Tan et al., 2024 — [arXiv:2211.12509](https://arxiv.org/abs/2211.12509)  
- **SwinLSTM**: Tang et al., 2023 — [arXiv:2308.09891](https://arxiv.org/abs/2308.09891)  
- **DCNN121**: Boulch et al., 2018 — [arXiv:1810.13273](https://arxiv.org/abs/1810.13273)  
- **OpenSTL**: Tan et al., 2023 — [arXiv:2306.11249](https://arxiv.org/abs/2306.11249)

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