Abstract:
Ionospheric delay is of concern for trans-ionospheric radio communication, especially for the navigation systems relying on these
satellite signals. Most of the ionospheric delays are estimated to a degree of first-order using dual frequency global navigation satellite
system (GNSS) receivers except during irregularities and equatorial plasma bubbles. The plasma bubbles are observed as a decrease or
reduction in total electron content (TEC) as a result of large-scale irregularities that are generated at the equatorial ionosphere. These
plasma bubbles can be detected from TEC values visually or by using mathematical algorithms. The mathematical algorithms may have
limitations based on assumptions made for the current dataset. Therefore, various machine learning (ML) techniques were tried by training
them with selected TEC depletions that are verified for accuracy. From this study, the Random Forest Method (RFM) has performed
well compared to other ML methods. The RFM is trained to use for the detection of TEC depletions from the Indian low-latitude region.
The training accuracy obtained is 97.6%, with a minimum classification error of 0.023%. The result obtained from the confusion matrix
ascertained that the proportion of positively classified cases that are truly positive that is the positive predictive value (PPV) is 96.8%.
These statistical results are validated after plotting the observed TEC depletion patch obtained from the ML method. There are cases in
which depletions detected by the ML method are appreciable over the mathematical algorithm. The ML technique once trained will not
have inherent limitations, as there are no assumptions or threshold values needed to set as required by most of the mathematical algorithms.
Thus, the results are encouraging and have scope for further improvement and advancement.
