Forecasting > 100 MeV SEP Events and Intensity based on CME and other Solar Activities using Machine Learning - Thesis Abstract

Aug 3, 2026

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High-intensity solar energetic particle (SEP) events create severe risks to astronauts and onboard machinery, requiring accurate forecasting of both occurrence and peak intensity. This thesis develops machine learning methods that leverage CME characteristics and related space-weather phenomena to classify SEP events and predict their peak intensity. The work addresses dataset scarcity through specialized classifier and regression strategies, including Richardson-forecasts and variants that incorporate learned Richardson error and retraining with DenseLoss and autoencoder-based approaches. Results demonstrate F1 performance of 0.800 for both event forecasting and peak-intensity prediction.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages145

Reading Time6 h 5 min

AccessFree to download

Abstract

Introduction

Motivation

Problem Statement

Approaches

Contributions

Organization

Related Work

SEP Forecasting

Machine Learning with Imbalanced Data

Explaining Predictions and Models

Description of SEP and CME Data

Features from DONKI and CDAW CME Catalogs

Feature value adjustments

Normalization of features

Forecasting SEP Events

Approaches

Experimental Evaluation

Forecasting SEP Intensities

Approaches

Florida Institute of Technology Scholarship Repository @ Florida Tech Theses and Dissertations 7-2023 Forecasting >100 MeV SEP Events and Intensity based on CMEand other Solar Activities using Machine Learning Daniel Lee Griessler Follow this and additional works at: [https://repository.fit.edu/etd](https://repository.fit.edu/etd) Part of the Computer Sciences Commons Forecasting > 100 MeV SEP Events and Intensity based on CME and other Solar Activities using Machine Learning by Daniel Lee Griessler Bachelor of Science Computer Science Florida Institute of Technology 2020 Bachelor of Science Mathematical Sciences Florida Institute of Technology 2020 A thesis submitted to the College Engineering and Science at Florida Institute of Technology in partial fulfillment of the requirements for the degree of Master of Science in Computer Science Melbourne, Florida July, 2023 © Copyright 2023 Daniel Lee Griessler All Rights Reserved The author grants permission to make single copies. We the undersigned committee hereby approve the attached thesis Forecasting > 100 MeV SEP Events and Intensity based on CME and other Solar Activities using Machine Learning by Daniel Lee Griessler Philip Chan, Ph.D. Associate Professor Electrical Engineering and Computer Science Major Advisor Ming Zhang, Ph.D. Professor Aerospace, Physics, and Space Sciences Philip J. Bernhard, Ph.D. Associate Professor and Department Head Electrical Engineering and Computer Science Abstract Title: Forecasting > 100 MeV SEP Events and Intensity based on CME and other Solar Activities using Machine Learning Author: Daniel Lee Griessler Major Advisor: Philip Chan, Ph.D. There is a severe risk for astronauts and machinery from high intensity Solar Energetic Particle (SEP) events which can be mitigated through accurate forecast of their presence and peak intensity. By using characteristics of CME and other space weather phenomena, machine learning techniques have the potential to classify and predict the peak intensity of SEP events. The extreme scarcity of SEP events in current datasets poses a challenge to traditional machine learning techniques. In this work, we first demonstrate classifier machine learning techniques that can achieve an F1 score of 0 .800 in forecasting SEP events. We then propose techniques for forecasting SEP peak intensity including Combining Richardson forecast (RC), learning Richardson Error (RE), and integrating retraining with DenseLoss (DL+rRT+AE) . Finally, we demonstrate through DL+rRT+AE that we can achieve the same F1 score of 0.800 for forecasting SEP peak intensity. Table of Contents Abstract ....................................... iii List of Figures ................................... ix List of Tables .................................... xi Acknowledgments ................................. xvii 1 Introduction ................................... 1 1.1 Motivation .................................. 1 1.2 Problem Statement ............................. 2 1.3 Approaches ................................. 2 1.4 Contributions ................................ 3 1.5 Organization ................................ 5 2 Related Work .................................. 6 2.1 SEP Forecasting ............................... 6 2.1.1 Using Characteristics of Solar Flares and X-rays ......... 6 2.1.2 Using Characteristics of CMEs .................. 9 2.1.3 Using Characteristics of Other Space Weather Phenomena ... 13 2.2 Machine Learning with Imbalanced Data ................. 14 2.2.1 Imbalanced Classification ...................... 14 2.2.2 Imbalanced Regression ....................... 19 2.3 Explaining Predictions and Models .................... 22 3 Description of SEP and CME Data .................... 24 3.1 Features from DONKI and CDAW CME Catalogs ............ 25 3.2 Features derived from DONKI and CDAW CME C

FAQs

为什么需要对>100 MeV的SEP事件及其强度进行预测？

高强度SEP事件会对宇航员与航天器设备造成严重风险，因此需要准确预测其发生与峰值强度，以降低影响并提升任务安全性。

论文使用了哪些空间天气特征来进行机器学习预测？

研究利用CME等空间天气现象的特征，并且包含来自DONKI与CDAW的CME目录特征，以及来自其他来源的特征，随后进行特征调整与归一化。

如何应对当前SEP数据集稀缺带来的不平衡学习挑战？

论文通过改进的分类与回归策略处理数据极少问题，例如在事件预测中使用过采样的神经网络，以及分类器/回归器再训练并结合自动编码器；在强度预测中还融入Richardson模型及其误差学习与重训练机制。

Aug 3, 2026

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