Prediction of plasmaspheric hiss spectral classes

We present a random forests machine learning model for prediction of plasmaspheric hiss spectral classes from the Van Allen Probes dataset. The random forests model provides accurate prediction of plasmaspheric hiss spectral classes obtained by the self organizing map (SOM) unsupervised machine learning classification technique. The high predictive skill of the random forests model is largely determined by the distinct and different locations of a given spectral class ( no hiss , regular hiss , and low-frequency hiss ) in (MLAT, MLT, L) coordinate space, which are the main predictors of the simplest and most accurate base model. Adding to such a base model any other single predictor among different magnetospheric, geomagnetic, and solar wind conditions provides only minor and similarly incremental improvements in predictive skill, which is comparable to the one obtained when including all possible predictors, and thus confirming major role of spatial location for accurate prediction.

Automated summary

We propose a random forests machine learning model for accurate prediction of plasmaspheric hiss spectral classes. The model's high predictive skill is largely determined by the spatial location of each spectral class, while the inclusion of other predictors only provides minor improvements in predictive skill.