Early Prediction of Geomagnetic Storms by Machine Learning Algorithms

TL;DR

This paper presents a machine learning approach using Random Forests and big data from ground stations to predict all types of geomagnetic storms reliably three hours in advance, addressing limitations of previous methods.

Contribution

It introduces a novel fusion of global solar measurement data with feature selection and downsampling to improve early prediction accuracy of geomagnetic storms.

Findings

Achieved 82.55% accuracy in early prediction three hours ahead.

Effectively predicted all types of geomagnetic storms.

Identified the practical limit of three-hour early prediction.

Abstract

Geomagnetic storms (GS) occur when solar winds disrupt Earth's magnetosphere. GS can cause severe damages to satellites, power grids, and communication infrastructures. Estimate of direct economic impacts of a large scale GS exceeds $40 billion a day in the US. Early prediction is critical in preventing and minimizing the hazards. However, current methods either predict several hours ahead but fail to identify all types of GS, or make predictions within short time, e.g., one hour ahead of the occurrence. This work aims to predict all types of geomagnetic storms reliably and as early as possible using big data and machine learning algorithms. By fusing big data collected from multiple ground stations in the world on different aspects of solar measurements and using Random Forests regression with feature selection and downsampling on minor geomagnetic storm instances (which carry majority of the data), we are able to achieve an accuracy of 82.55% on data collected in 2021 when making early predictions three hours in advance. Given that important predictive features such as historic Kp indices are measured every 3 hours and their importance decay quickly with the amount of time in advance, an early prediction of 3 hours ahead of time is believed to be close to the practical limit.