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CLARE: Classification-based Regression for Electron Temperature Prediction

arXiv:2603.12470v1 Announce Type: cross Abstract: Electron temperature (Te) is an important parameter governing space weather in the upper atmosphere, but has historically been underexplored in the space weather machine learning literature. We present CLARE, a machine learning model for predicting electron temperature in the Earth's plasmasphere trained on AKEBONO (EXOS-D) satellite measurements as well as solar and geomagnetic indices. CLARE uses a classification-based regression architecture that transforms the continuous Te output space into 150 discrete classification intervals. Training the model on a classification task improves prediction accuracy by 6.46% relative compared to a traditional regression model while also outputting uncertainty estimation information on its predictions. On a held out test set from the AKEBONO data, the model's Te predictions achieve 69.67% accuracy within 10% of the ground truth and 46.17% on a known geomagnetic storm period from January 30th to Fe

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Michael Liang, Blake DeHaas, Naomi Maruyama, Xiangning Chu, Takumi Abe, Koh-Ichiro Oyama · · 1 min read · 8 views
#physics.space-ph #cs.AI

arXiv:2603.12470v1 Announce Type: cross Abstract: Electron temperature (Te) is an important parameter governing space weather in the upper atmosphere, but has historically been underexplored in the space weather machine learning literature. We present CLARE, a machine learning model for predicting electron temperature in the Earth's plasmasphere trained on AKEBONO (EXOS-D) satellite measurements as well as solar and geomagnetic indices. CLARE uses a classification-based regression architecture that transforms the continuous Te output space into 150 discrete classification intervals. Training the model on a classification task improves prediction accuracy by 6.46% relative compared to a traditional regression model while also outputting uncertainty estimation information on its predictions. On a held out test set from the AKEBONO data, the model's Te predictions achieve 69.67% accuracy within 10% of the ground truth and 46.17% on a known geomagnetic storm period from January 30th to February 7th, 1991. We show that machine learning can be used to produce high-accuracy Te models on publicly available data.

Executive Summary

The article introduces CLARE, a machine learning model for predicting electron temperature in the Earth's plasmasphere. Trained on satellite measurements and solar and geomagnetic indices, CLARE achieves high accuracy and provides uncertainty estimation. The model's classification-based regression architecture improves prediction accuracy by 6.46% compared to traditional regression models. CLARE's performance is evaluated on a held-out test set, achieving 69.67% accuracy within 10% of the ground truth. The study demonstrates the potential of machine learning in producing high-accuracy models for electron temperature prediction using publicly available data.

Key Points

  • ▸ CLARE is a machine learning model for predicting electron temperature in the Earth's plasmasphere
  • ▸ The model uses a classification-based regression architecture to improve prediction accuracy
  • ▸ CLARE achieves 69.67% accuracy within 10% of the ground truth on a held-out test set

Merits

Improved Prediction Accuracy

CLARE's classification-based regression architecture improves prediction accuracy by 6.46% compared to traditional regression models

Uncertainty Estimation

The model provides uncertainty estimation information on its predictions, which is valuable for decision-making

Demerits

Limited Data

The model is trained on a limited dataset from the AKEBONO satellite, which may not be representative of all space weather conditions

Complexity

The classification-based regression architecture may be more complex to implement and interpret than traditional regression models

Expert Commentary

The article presents a significant contribution to the field of space weather prediction, demonstrating the potential of machine learning in producing high-accuracy models for electron temperature prediction. The use of a classification-based regression architecture is a novel approach that improves prediction accuracy and provides uncertainty estimation. However, the model's performance is limited by the quality and quantity of the training data, and further research is needed to evaluate its performance on more diverse and representative datasets. Overall, CLARE's performance has important implications for the development of more accurate space weather forecasting models and the use of machine learning in this field.

Recommendations

  • ✓ Further evaluation of CLARE's performance on more diverse and representative datasets
  • ✓ Investigation of the potential applications of CLARE's uncertainty estimation in space weather-related decision-making
  • ✓ Development of more complex machine learning models that incorporate multiple parameters and datasets to improve space weather prediction accuracy

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