Amplitude Scintillation Forecasting Using Bagged Trees
This work addresses forecasting ionospheric signal fluctuations for GNSS users, but it is incremental as it applies existing ML methods to a specific domain problem.
The paper tackled forecasting amplitude scintillation severity (weak, moderate, or severe) using historical S4 index data from a single GPS receiver, achieving accuracies of 81% with a balanced dataset and 97% with an imbalanced dataset using a bagged trees model.
Electron density irregularities present within the ionosphere induce significant fluctuations in global navigation satellite system (GNSS) signals. Fluctuations in signal power are referred to as amplitude scintillation and can be monitored through the S4 index. Forecasting the severity of amplitude scintillation based on historical S4 index data is beneficial when real-time data is unavailable. In this work, we study the possibility of using historical data from a single GPS scintillation monitoring receiver to train a machine learning (ML) model to forecast the severity of amplitude scintillation, either weak, moderate, or severe, with respect to temporal and spatial parameters. Six different ML models were evaluated and the bagged trees model was the most accurate among them, achieving a forecasting accuracy of $81\%$ using a balanced dataset, and $97\%$ using an imbalanced dataset.