Geomagnetic Activity Forecasting Using Self-Learning Algorithms: Application in Space Weather Studies

Details Geomagnetic Activity Forecasting Using Self-Learning Algorithms: Application in Space Weather Studies Geomagnetic Activity Forecasting Using Self-Learning Algorithms: Application in Space Weather Studies

May 2005

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2005agusmsm51a..09k&link_type=abstract

American Geophysical Union, Spring Meeting 2005, abstract #SM51A-09

Statistics

Computation

2447 Modeling And Forecasting, 2722 Forecasting, 2788 Storms And Substorms, 2794 Instruments And Techniques

Scientific paper

The ability to forecast the geomagnetic activities is becoming more important as human activity in space becomes more prevalent. For example, early warning of geomagnetic storms could help mitigate their harmful effects on space electronics and on electrical power lines. Moreover, recently developed space weather algorithms that utilize physics-based models require future values of Kp as an input in order to forecast the ionospheric behavior. Computational learning theory and data-driven modeling techniques are new and rapidly expanding areas of research that aim at developing efficient learning algorithms. Here we compare self-learning algorithms regarding their abilities to forecast the level of geomagnetic activities, as represented by Kp. In particular, we consider the following algorithms: artificial neural networks, locally weighted projection regression, support vector machines, and relevance vector machines. Different parameters are considered such as: (1) length of forecasting time, (2) type and size of input data, and (3) training set size. These learning machines are compared regarding their generalization capabilities and structure reliabilities. The relative strengths and limitations of these algorithms will be presented.

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