Optimization of the neural‐network geomagnetic model for forecasting large‐amplitude substorm events

Abstract

Artificial neural networks (NN) have been used to model solar wind‐driven auroral electrojet dynamics, with emphasis on the reliable real‐time forecasting of auroral electrojet activity (the AE index) from solar wind input. Practical limitations of the NN‐based models used earlier are clarified. These include the inability to accurately predict large‐amplitude substorm events, which is the most important feature for many applications. A novel technique for improving predictions is suggested based on application‐specific threshold mapping and symbolic encoding of the AE index. This approach allows us to disregard relatively unimportant details of small‐amplitude perturbations and effectively improve forecasting of large‐amplitude events. Results from our new model imply that application‐oriented optimization of the real‐time substorm forecasting system can be an important factor in the overall improvement of the prediction accuracy.