Comparative Analysis of the Procedures to Forecast the Kp Geomagnetic Index by Machine Learning

Abstract

Geomagnetic disturbances are one of the most important factors in space weather, the role of which will increase with the development of the space industry and the global digital industry, both on Earth and in near-Earth space. Geomagnetic activity is usually characterized by special indices. One of the most common geomagnetic indices is the Kp index, first introduced by Julius Bartels in 1939. In this study, we explore the possibility of predicting the following Kp index values during the next day using machine learning models based on the hourly values of the parameters of solar wind and interplanetary magnetic field, and of the hourly Dst index. We use such ML models as linear regression, gradient boosting and multilayer perceptrons. We test to what extent the use of history of time series improves the performance of ML models. We draw conclusions about the optimal procedure of creating and applying of a machine learning model to solve the Kp index forecasting problem. The best results by most of the quality metrics were demonstrated by CatBoost and perceptron with two hidden layers. The most significant input features detected were preceding values of the Kp index itself, solar wind velocity and density, modulus and z-component of the interplanetary magnetic field.