One day prediction of nighttime VLF amplitudes using nonlinear autoregression and neural network modeling

Abstract

The electric field amplitude of very low frequency (VLF) transmitter from Hawaii (NPM) has been continuously recorded at Chofu (CHF), Tokyo, Japan. The VLF amplitude variability indicates lower ionospheric perturbation in the D region (60–90 km altitude range) around the NPM‐CHF propagation path. We carried out the prediction of daily nighttime mean VLF amplitude by using Nonlinear Autoregressive with Exogenous Input Neural Network (NARX NN). The NARX NN model, which was built based on the daily input variables of various physical parameters such as stratospheric temperature, total column ozone, cosmic rays, Dst, and Kp indices possess good accuracy during the model building. The fitted model was constructed within the training period from 1 January 2011 to 4 February 2013 by using three algorithms, namely, Bayesian Neural Network (BRANN), Levenberg Marquardt Neural Network (LMANN), and Scaled Conjugate Gradient (SCG). The LMANN has the largest Pearson correlation coefficient (r) of 0.94 and smallest root‐mean‐square error (RMSE) of 1.19 dB. The constructed models by using LMANN were applied to predict the VLF amplitude from 5 February 2013 to 31 December 2013. As a result the one step (1 day) ahead predicted nighttime VLF amplitude has the r of 0.93 and RMSE of 2.25 dB. We conclude that the model built according to the proposed methodology provides good predictions of the electric field amplitude of VLF waves for NPM‐CHF (midlatitude) propagation path.

Simultaneous Study of VLF/ULF Anomalies Associated with Earthquakes in Japan

“…and depth greater than 35 km. However, there are cases when amplitude anomalies (a significant decrease in trend value) occurred without seismic activities as shown in Figure 2, the reasons of these anomalies may be due to the factors other than seismic activities such as the effect of global geomagnetic disturbance originated from solar activity and atmospheric phenomena toward the lower ionosphere as reported by [28] [29].…”

“…The reduction method was further refined by trend and nighttime fluctuation methods [27]. In spite of using rather sophisticated data analysis, there are still false seismogenic anomalies because of many perturbation sources of the lower ionosphere other than seismogenic ones such as space weather and atmospheric parameters [28] [29]. Therefore, multi-parameter observations are promising to identify the seismo-electromagnetic signals more accurately rather than observations of single parameter.…”

Simulation of diurnal variation of sub-ionospheric VLF transmitter signals using machine learning approach

“…Various studies were done in the past to predict ionospheric peak electron density (NmF2), peak height (hmF2), critical frequency (foF2), total electron content (TEC) using an artificial neural network (ANN) model [13][14][15][16][17][18]. Santosa & Hobara (2017) ). During geomagnetic storms, energetic particle precipitation in the ionosphere also change the ionospheric profiles and disturb the VLF signals [20,21].…”