Ionospheric Scintillation Forecasting Using Machine Learning

TL;DR

This paper develops a machine learning model, specifically XGBoost, to forecast ionospheric amplitude scintillation severity levels using GNSS data, achieving 77% accuracy and improving GNSS reliability.

Contribution

The study introduces a novel ML-based forecasting approach for ionospheric scintillation severity, with XGBoost outperforming other models in prediction accuracy.

Findings

XGBoost achieved 77% prediction accuracy.

Machine learning effectively predicts scintillation severity.

The model enhances GNSS signal reliability.

Abstract

This study explores the use of historical data from Global Navigation Satellite System (GNSS) scintillation monitoring receivers to predict the severity of amplitude scintillation, a phenomenon where electron density irregularities in the ionosphere cause fluctuations in GNSS signal power. These fluctuations can be measured using the S4 index, but real-time data is not always available. The research focuses on developing a machine learning (ML) model that can forecast the intensity of amplitude scintillation, categorizing it into low, medium, or high severity levels based on various time and space-related factors. Among six different ML models tested, the XGBoost model emerged as the most effective, demonstrating a remarkable 77% prediction accuracy when trained with a balanced dataset. This work underscores the effectiveness of machine learning in enhancing the reliability and performance of GNSS signals and navigation systems by accurately predicting amplitude scintillation severity.