Ionospheric Scintillation Prediction Using Decision Tree and Rainforest Techniques

The ionosphere contains electron density variation. When radio signals transmitted from global navigation satellite systems (GNSS) pass through such medium, additional delays are added. With ionospheric irregularity, fluctuation in GNSS signals known as scintillation are often observed resulting in reduced number of tracked satellites then degrade positioning performances. At present, scintillation is considered random, hence, the ability to detect or predict such phenomenon is crucial to efficient system operation. In this research, we design machine learning algorithm for scintillation prediction. Both Decision Tree (DT) and Random Decision Forest (RF), are implemented to predict daily ionospheric scintillation at King Mongkut’s Institute of Technology Ladkrabang (KMITL) station in Thailand $\left(13.73 \circ \mathrm{E}, 100.77{}^{\circ} \mathbf{N}\right)$. The rate of total electron content change index (ROTI) is also used. Modeling is carried out for four months in March (equinox), June (solstice), September (equinox), and December (solstice) in 2022, representing different seasons in space weather study. The prediction results are evaluated using the S 4 index observations at KMITL station and then compared between DT and RF methods. The designed model has a high potential for scintillation prediction.