Summary of Early Prediction Of Geomagnetic Storms by Machine Learning Algorithms, By Iris Yan
Early Prediction of Geomagnetic Storms by Machine Learning Algorithms
by Iris Yan
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to predicting geomagnetic storms using big data and machine learning algorithms aims to accurately forecast all types of GS reliably and as early as possible. By fusing data from multiple ground stations on solar measurements, Random Forests regression with feature selection and downsampling is used to achieve an accuracy of 82.55% when making predictions three hours in advance. This early prediction capability is believed to be close to the practical limit due to the decay of important predictive features like historic Kp indices over time. The work has significant implications for preventing and minimizing hazards caused by GS, which can result in severe damages to satellites, power grids, and communication infrastructures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Geomagnetic storms happen when solar winds affect Earth’s magnetic field. These storms can cause big problems for satellites, power grids, and communication systems. It’s really important to predict when a storm will happen so we can prepare and minimize the damage. Right now, there are only two ways to make predictions: either very far in advance but not very accurate or just one hour before the storm hits. This new method uses lots of data from different places around the world and special computer algorithms to make more accurate predictions up to three hours before the storm happens. |
Keywords
* Artificial intelligence * Feature selection * Machine learning * Regression
