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\begin{document}

\title{Simulation of diurnal variation of sub-ionospheric VLF transmitter
signals using machine learning approach}



\author[1]{Sujay Pal}%
\author[2]{Kinsuk Giri}%
\affil[1]{Srikrishna College, Nadia, WB, India}%
\affil[2]{NITTTR Kolkata}%


\vspace{-1em}



  
  \date{May 22, 2024}


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\begin{abstract}
This paper shows simulation models for diurnal variation of
sub-ionospheric Very Low Frequency (VLF) signals using machine learning
approach. Recording of VLF transmitter signals using a ground-based
radio receiver provides a beautiful and cost-effective way of monitoring
the lower ionosphere (D/E regions) in the altitude range (60-90 km). VLF
signals respond to the ionization variations due to the Sun and other
terrestrial or extra-terrestrial sources. Consequently, it has many
applications in remote sensing of the lower ionosphere. Therefore,
predicting or simulating the diurnal variation of VLF transmitter
signals using past data will help to understand the variability of the
ionosphere. Here, the VLF signal from the Indian transmitter VTX (18.2
kHz) received at Kolkata is used for the training, validating, and
testing purposes in the machine learning models. Two predictive models,
multiple linear regression (MLR) and artificial neural network (ANN)
have been built and Pearson correlation coefficients outside the
training range are obtained as R=0.94 and R=0.93 respectively for the
two models. Variation of the VLF transmitter signal is also calculated
using the well-known Long Wave Propagation Capability (LWPC) code
coupled with the International Reference Ionosphere (IRI-2016) model and
the same is compared with the MLR and ANN model predictions. Both the
MLR and ANN models are found to be performing better than the LWPC
simulation.%
\end{abstract}%



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