Machine learning regression models for prediction of multiple ionospheric parameters
The variation of the ionospheric parameters has a crucial role in space weather,
communication, and navigation applications. In this research, we analyze the prediction
performance of three machine learning (ML) regression models, decision trees, random
forest and support vector machine algorithms for the F2-layer critical frequency (f0F2), F2-
layer height of the peak electron density (hmF2), and total electron content (TEC). The hourly
f0F2 and hmF2 values of ROME (RO041) digisonde and hourly TEC values of a close by …
communication, and navigation applications. In this research, we analyze the prediction
performance of three machine learning (ML) regression models, decision trees, random
forest and support vector machine algorithms for the F2-layer critical frequency (f0F2), F2-
layer height of the peak electron density (hmF2), and total electron content (TEC). The hourly
f0F2 and hmF2 values of ROME (RO041) digisonde and hourly TEC values of a close by …
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