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Found 11 papers, 5 papers with code
Active Region-based Flare Forecasting with Sliding Window Multivariate Time Series Forest Classifiers
Our findings demonstrate that our sliding-window time series forest classifier performs effectively in solar flare prediction (with a True Skill Statistic of over 85\%) while also pinpointing the most crucial features and sub-intervals for a given learning task.
Unveiling the Potential of Deep Learning Models for Solar Flare Prediction in Near-Limb Regions
We trained three well-known deep learning architectures--AlexNet, VGG16, and ResNet34 using transfer learning and compared and evaluated the overall performance of our models using true skill statistics (TSS) and Heidke skill score (HSS) and computed recall scores to understand the prediction sensitivity in central and near-limb regions for both X- and M-class flares.
Towards Interpretable Solar Flare Prediction with Attention-based Deep Neural Networks
In this work, we developed an attention-based deep learning model as an improvement over the standard convolutional neural network (CNN) pipeline to perform full-disk binary flare predictions for the occurrence of $\geq$M1. 0-class flares within the next 24 hours.
Exploring Deep Learning for Full-disk Solar Flare Prediction with Empirical Insights from Guided Grad-CAM Explanations
This study progresses solar flare prediction research by presenting a full-disk deep-learning model to forecast $\geq$M-class solar flares and evaluating its efficacy on both central (within $\pm$70$^\circ$) and near-limb (beyond $\pm$70$^\circ$) events, showcasing qualitative assessment of post hoc explanations for the model's predictions, and providing empirical findings from human-centered quantitative assessments of these explanations.
Explainable Deep Learning-based Solar Flare Prediction with post hoc Attention for Operational Forecasting
This paper presents a post hoc analysis of a deep learning-based full-disk solar flare prediction model.
Explaining Full-disk Deep Learning Model for Solar Flare Prediction using Attribution Methods
This paper contributes to the growing body of research on deep learning methods for solar flare prediction, primarily focusing on highly overlooked near-limb flares and utilizing the attribution methods to provide a post hoc qualitative explanation of the model's predictions.
Towards Coupling Full-disk and Active Region-based Flare Prediction for Operational Space Weather Forecasting
We utilized an equal weighted average ensemble of two base learners' flare probabilities as our baseline meta learner and improved the capabilities of our two base learners by training a logistic regression model.
All-Clear Flare Prediction Using Interval-based Time Series Classifiers
An all-clear flare prediction is a type of solar flare forecasting that puts more emphasis on predicting non-flaring instances (often relatively small flares and flare quiet regions) with high precision while still maintaining valuable predictive results.
How to Train Your Flare Prediction Model: Revisiting Robust Sampling of Rare Events
We present a case study of solar flare forecasting by means of metadata feature time series, by treating it as a prominent class-imbalance and temporally coherent problem.
Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations
The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology, Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers, expert modelers, and computer/data scientists.
Challenges with Extreme Class-Imbalance and Temporal Coherence: A Study on Solar Flare Data
This is in particular prevalent in interdisciplinary research where the theoretical aspects are sometimes overshadowed by the challenges of the application.
