A logistic regression model for predicting the occurrence of intense geomagnetic storms

• Main Author: N. Srivastava
• Format: Article
• Language: English
• Published: Copernicus Publications 2005-11-01
• Series: Annales Geophysicae
• Online Access: https://www.ann-geophys.net/23/2969/2005/angeo-23-2969-2005.pdf
• ISSN: 0992-7689; 1432-0576

A logistic regression model is implemented for predicting the occurrence of intense/super-intense geomagnetic storms. A binary dependent variable, indicating the occurrence of intense/super-intense geomagnetic storms, is regressed against a series of independent model variables that define a number of solar and interplanetary properties of geo-effective CMEs. The model parameters (regression coefficients) are estimated from a training data set which was extracted from a dataset of 64 geo-effective CMEs observed during 1996-2002. The trained model is validated by predicting the occurrence of geomagnetic storms from a validation dataset, also extracted from the same data set of 64 geo-effective CMEs, recorded during 1996-2002, but not used for training the model. The model predicts 78% of the geomagnetic storms from the validation data set. In addition, the model predicts 85% of the geomagnetic storms from the training data set. These results indicate that logistic regression models can be effectively used for predicting the occurrence of intense geomagnetic storms from a set of solar and interplanetary factors.