用分类回归提升电离层电子温度预测精度
CLARE: Classification-based Regression for Electron Temperature Prediction
- 将连续温度预测转为150类分类任务，提升建模效率
- 测试集准确率69.67%，地磁暴期间达46.17%
- 适合空间天气建模与卫星数据挖掘者使用
电子温度（Te）是影响高层大气空间天气的关键参数，但在机器学习研究中长期被忽视。本文提出CLARE模型，基于AKEBONO（EXOS-D）卫星观测数据及太阳与地磁指数，实现地球等离子体层电子温度预测。该模型采用基于分类的回归架构，将连续输出空间划分为150个离散分类区间。相比传统回归模型，预测准确率相对提升6.46%，并能输出预测不确定性。在留出测试集上，预测值与真实值偏差小于10%的准确率达69.67%；在1991年1月30日至2月7日已知地磁暴期，准确率为46.17%。结果表明，利用公开数据可构建高精度电子温度机器学习模型。
原文摘要 · Abstract (English)
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.
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