Predicting the activity of solar flares is of great significance for studying its physical mechanism and the impact on human production and life. Problems such as class imbalance, high time-series sensitivity, and over-localization of important features exist in the sample data used for flare forecasting. We design a solar flare fusion method based on resampling and CNN-GRU algorithm to try to solve the above problems. In order to verify the effectiveness of this fusion method, first, we compared the forecast performance of different resampling methods by keeping the forecast model unchanged. Then, we used the resampling algorithm with high performance to combine some single forecast models and fusion forecast models respectively. We use the 2010-2017 sunspot dataset to train and test the performance of the flare model in predicting flare events in the next 48 hours. Through the conclusion of the above steps, we prove that the resampling method SMOTE and its variant SMOTE-ENN are more advantageous in class imbalance problem of flare samples. In addition, after the fusion of one-dimensional convolution and recurrent network with 'forget-gate' , combined with the SMOTE-ENN to achieve TSS=61%, HSS=61% , TP_Rate = 77% and TN_Rate = 83%. This proves that the fusion model based on resampling and CNN-GRU algorithm is more suitable for solar flare forecasting.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.