3 research outputs found
Martian Ionosphere Electron Density Prediction Using Bagged Trees
The availability of Martian atmospheric data provided by several Martian
missions broadened the opportunity to investigate and study the conditions of
the Martian ionosphere. As such, ionospheric models play a crucial part in
improving our understanding of ionospheric behavior in response to different
spatial, temporal, and space weather conditions. This work represents an
initial attempt to construct an electron density prediction model of the
Martian ionosphere using machine learning. The model targets the ionosphere at
solar zenith ranging from 70 to 90 degrees, and as such only utilizes
observations from the Mars Global Surveyor mission. The performance of
different machine learning methods was compared in terms of root mean square
error, coefficient of determination, and mean absolute error. The bagged
regression trees method performed best out of all the evaluated methods.
Furthermore, the optimized bagged regression trees model outperformed other
Martian ionosphere models from the literature (MIRI and NeMars) in finding the
peak electron density value, and the peak density height in terms of
root-mean-square error and mean absolute error.Comment: The peer-reviewed paper is available at:
https://doi.org/10.1109/ICECTA57148.2022.999050
Amplitude Scintillation Forecasting Using Bagged Trees
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 using a
balanced dataset, and using an imbalanced dataset.Comment: This paper was presented at IGARSS 2022, Kuala Lumpur, Malaysia. doi:
10.1109/IGARSS46834.2022.988338