Amplitude Scintillation Forecasting Using Bagged Trees

TL;DR

This paper explores forecasting amplitude scintillation severity in the ionosphere using machine learning, demonstrating that bagged trees provide high accuracy with historical GPS data, aiding GNSS reliability.

Contribution

It introduces a machine learning approach, specifically bagged trees, for predicting ionospheric scintillation severity from single-receiver data, outperforming other models.

Findings

Bagged trees achieved 81% accuracy on balanced data.

Forecast accuracy increased to 97% with imbalanced data.

Single-receiver historical data can effectively predict scintillation severity.

Abstract

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.