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

N. Srivastava (1)

1 Udaipur Solar Observatory

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.

Domaines: Océan, Atmosphère; Sciences de la Terre

N. Srivastava. A logistic regression model for predicting the occurrence of intense geomagnetic storms. Annales Geophysicae, 2005, 23 (9), pp.2969-2974. ⟨hal-00317972⟩