Towards coupling full-disk and active region-based flare prediction for operational space weather forecasting
نویسندگان
چکیده
Solar flare prediction is a central problem in space weather forecasting and has captivated the attention of wide spectrum researchers due to recent advances both remote sensing as well machine learning deep approaches. The experimental findings based on models reveal significant performance improvements for task specific datasets. Along with building models, practice deploying such production environments under operational settings more complex often time-consuming process which not addressed directly research settings. We present set new heuristic approaches train deploy an solar system $\geq$M1.0-class flares two modes: full-disk active region-based. In mode, predictions are performed line-of-sight magnetograms using whereas region-based issued each region individually multivariate time series data instances. outputs from individual forecasts predictors combined final result meta-model. utilized equal weighted average ensemble base learners' probabilities our baseline meta learner improved capabilities learners by training logistic regression model. major this study are: (i) successfully coupled heterogeneous trained different datasets model architecture predict probability next 24 hours, (ii) Our proposed ensembling model, i.e., regression, improves predictive measured terms widely used metrics True Skill Statistic (TSS) Heidke core (HSS), (iii) analysis suggests that regression-based (Meta-FP) (base learner) $\sim9\%$ TSS $\sim10\%$ HSS. Similarly, it AR-based $\sim17\%$ $\sim20\%$ HSS respectively. Finally, when compared $\sim15\%$.
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ژورنال
عنوان ژورنال: Frontiers in Astronomy and Space Sciences
سال: 2022
ISSN: ['2296-987X']
DOI: https://doi.org/10.3389/fspas.2022.897301