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Abstract Solar energetic particle (SEP) events, in particular high-energy-range SEP events, pose significant risks to space missions, astronauts, and technological infrastructure. Accurate prediction of these high-impact events is crucial for mitigating potential hazards. In this study, we present an end-to-end ensemble machine learning (ML) framework for the prediction of high-impact ∼100 MeV SEP events. Our approach leverages diverse data modalities sourced from the Solar and Heliospheric Observatory and the Geostationary Operational Environmental Satellite integrating extracted active region polygons from solar extreme ultraviolet (EUV) imagery, time-series proton flux measurements, sunspot activity data, and detailed active region characteristics. To quantify the predictive contribution of each data modality (e.g., EUV or time series), we independently evaluate them using a range of ML models to assess their performance in forecasting SEP events. Finally, to enhance the SEP predictive performance, we train an ensemble learning model that combines all the models trained on individual data modalities, leveraging the strengths of each data modality. Our proposed ensemble approach shows promising performance, achieving a recall of 0.80 and 0.75 in balanced and imbalanced settings, respectively, underscoring the effectiveness of multimodal data integration for robust SEP event prediction and enhanced forecasting capabilities. 

The purpose of this study is to provide a comprehensive resource for the selection of data representations for machine learning-oriented models and components in solar flare prediction tasks. Major solar flares occurring in the solar corona and heliosphere can bring potential destructive consequences, posing significant risks to astronauts, space stations, electronics, communication systems, and numerous technological infrastructures. For this reason, the accurate detection of major flares is essential for mitigating these hazards and ensuring the safety of our technology-dependent society. In response, leveraging machine learning techniques for predicting solar flares has emerged as a significant application within the realm of data science, relying on sensor data collected from solar active region photospheric magnetic fields by space- and ground-based observatories. In this research, three distinct solar flare prediction strategies utilizing the photospheric magnetic field parameter-based multivariate time series dataset are evaluated, with a focus on data representation techniques. Specifically, we examine vector-based, time series-based, and graph-based approaches to identify the most effective data representation for capturing key characteristics of the dataset. The vector-based approach condenses multivariate time series into a compressed vector form, the time series representation leverages temporal patterns, and the graph-based method models interdependencies between magnetic field parameters. The results demonstrate that the vector representation approach exhibits exceptional robustness in predicting solar flares, consistently yielding strong and reliable classification outcomes by effectively encapsulating the intricate relationships within photospheric magnetic field data when coupled with appropriate downstream machine learning classifiers. 

Abstract Solar energetic particle (SEP) events pose significant risks to both space and ground-level infrastructure, as well as to human health in space. Understanding and predicting these events are critical for mitigating their potential impacts. In this paper, we address the challenge of predicting SEP events using proton flux data. We leverage some of the most recent advances in time series data mining, such as shapelets and the matrix profile, to propose a simple and easily understandable prediction approach. Our objective is to mitigate the interpretability challenges inherent to most machine learning models and to show that other methods exist that can not only yield accurate forecasts but also facilitate exploration and insight generation within the data domain. For this purpose, we construct a multivariate time series data set consisting of proton flux data recorded by the National Oceanic and Atmospheric Administration's geosynchronous orbit Earth-observing satellite. Then, we use our proposed approach to mine shapelets and make predictions using a random forest classifier. We demonstrate that our approach rivals state-of-the-art SEP prediction, offering superior interpretability and the ability to predict SEP events before their parent eruptive flares. 

Solar flares are significant occurrences in solar physics, impacting space weather and terrestrial technologies. Accurate classification of solar flares is essential for predicting space weather and minimizing potential disruptions to communication, navigation, and power systems. This study addresses the challenge of selecting the most relevant features from multivariate time-series data, specifically focusing on solar flares. We employ methods such as Mutual Information (MI), Minimum Redundancy Maximum Relevance (mRMR), and Euclidean Distance to identify key features for classification. Recognizing the performance variability of different feature selection techniques, we introduce an ensemble approach to compute feature weights. By combining outputs from multiple methods, our ensemble method provides a more comprehensive understanding of the importance of features. Our results show that the ensemble approach significantly improves classification performance, achieving values 0.15 higher in True Skill Statistic (TSS) values compared to individual feature selection methods. Additionally, our method offers valuable insights into the underlying physical processes of solar flares, leading to more effective space weather forecasting and enhanced mitigation strategies for communication, navigation, and power system disruptions.