Solar Flare Prediction using Machine Learning Algorithms on RHESSI Dataset

Machine learning has been in the industry for a long time and has been applied in diverse sectors, including traffic control, self-automated automobiles, recommender systems, security control, space exploration, etc. In the space exploration domain, the predictions made by machine learning and deep learning approaches using the videos captured by observatories help discover new theories about space. Solar Flare prediction is one of the machine learning applications. Solar flare prediction is complex, and the exact reason behind these massive eruptions is still a mystery. Solar flares are detectable through a telescope, and the particles scattered after the eruptions can reach the Earth within eight minutes. The frequent disturbances in the active spot of the sun can result in numerous disruptions on Earth, including power outages, disruptions in communication systems, etc. Therefore, it becomes crucial to have preliminary information on solar flares so that timely preparation can be done to combat their effects on Earth. In this work, we analyze the various machine learning techniques and deep learning approaches for solar flares prediction using the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI) dataset.