EoFNets: EyeonFlare Networks to predict solar flare using Temporal Convolutional Network (TCN)
Solar Active Regions are characterised by their intense magnetic activity, which often leads to solar phenomena such as solar flares, and coronal mass ejections (CMEs). With the recent advancement of computing technologies and the huge integration of Artificial Intelligence (AI), many approaches hav...
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Published in: | 2024 10th International Conference on Control, Decision and Information Technologies (CoDIT) pp. 1120 - 1126 |
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Main Authors: | , , , , , , , |
Format: | Conference Proceeding |
Language: | English |
Published: |
IEEE
01-07-2024
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Subjects: | |
Online Access: | Get full text |
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Summary: | Solar Active Regions are characterised by their intense magnetic activity, which often leads to solar phenomena such as solar flares, and coronal mass ejections (CMEs). With the recent advancement of computing technologies and the huge integration of Artificial Intelligence (AI), many approaches have been proposed for forecasting solar eruptions using machine learning. In this study, we propose the use of a Temporal Convolutional Network (TCN) for predicting whether an active region will be flaring in a specific window of time and defining the flare class. The dataset is categorised into three different subsets based on the flare class and trained separately with the same TCN architecture to apply late fusion. The proposed solar flare prediction ensemble (EoFNets) is based on both the physical characteristics of the active region (EoFPhyNet) and geometric features (EoFGeoNet). Experimental results show that TCN outperforms long short-term memory (LSTM) in three cases. Our main aim is to deploy deep-learning-based approaches onboard for faster and more accurate real-time monitoring as well as leveraging the higher sampling rates for improved time-series predictions. Many major benefits can be realised if the deep learning models can be implemented onboard, including a sizeable reduction in the volume of downlinked data, and improved system latency. However, implementing deep learning models in space can be a critical task, as most approaches require high computational and memory resources, both of which are limited in typical spacecraft onboard data handling systems. Nevertheless, the EoFNets network outlined in this paper has been optimised to fit the resource constraints of a space platform deployed at the extreme edge far from Earth. Two low-power hardware targets are considered, namely the IntelMovidius MyriadX and Rockchip RK3588S. To the best of our knowledge, this is the first time that such a TCN network has been proposed for solar flare forecasting. |
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ISSN: | 2576-3555 |
DOI: | 10.1109/CoDIT62066.2024.10708396 |