Irregularities in electron density usually correlate with ionospheric plasma perturbations. These variations causing radio signal fluctuations, in response, generate ionospheric scintillations that frequently occur in low-latitude regions. In this research, the combination of an artificial neural network (ANN) with the genetic algorithm (GA) was implemented to predict ionospheric scintillations. The GA method was considered for obtaining the ANN model’s initial weights. This procedure was applied to GNSS observations at GUAM (13.58°E, 144.86°N, 201.922H) station for the daily prediction of ionospheric amplitude scintillations via predicting the signal-to-noise ratio (S4) or via prediction of the rate of TEC index (ROTI). Thirty-day modeling was carried out for three months in January, March, and July, representing different seasons of the winter solstice, equinox, and summer solstice during three different years, 2015, 2017, and 2020, with different solar activities. The models, along with ionospheric physical data, were used for the daily prediction of ionospheric scintillations for the consequent day after the modeling. The prediction results were evaluated using S4 derived from GNSS observations at GUAM station. The designed model has the ability to predict daily ionospheric scintillations with an accuracy of about 81% for the S4 and about 80% for the ROTI.
Sea surface currents are often modeled using numerical models without adequately addressing the issue of model calibration at the regional scale. The aim of this study is to calibrate the MIKE 21 numerical ocean model for the Persian Gulf and the Oman Sea to improve the sea surface currents obtained from the model. The calibration was performed through data assimilation of the model with altimetry and hydrographic observations using variational data assimilation, where the weights of the objective functions were defined based on the type of observations and optimized using metaheuristic optimization methods. According to the results, the calibration of the model generally led the model results closer to the observations. This was reflected in an improvement of about 0.09 m/s in the obtained sea surface currents. It also allowed for more accurate evaluations of model parameters, such as Smagorinsky and Manning coefficients. Moreover, the root mean square error values between the satellite altimetry observations at control stations and the assimilated model varied between 0.058 and 0.085 m. We further showed that the kinetic energy produced by sea surface currents could be used for generating electricity in the Oman Sea and near Jask harbor.
<p>Ionospheric irregularities can be caused by from sun activity variations, which may cause irregularities in electron density within the ionospheric layer and, subsequently, plasma perturbations. Typical examples of these irregularities are ionospheric scintillations. The ionospheric irregularities can cause fluctuations in the signal intensity transmitted from the satellite by reducing the signal-to-noise ratio. In addition, scintillation can lead to extreme fluctuations in the phase of a signal transmitted. Ionospheric irregularities originate destructive effects on radio signals transmitted from global navigation satellite systems (GNSS). This phenomenon can generate fluctuations in the signal intensity transmitted from the satellite by decreasing the signal-to-noise ratio of the transmitted wave. The primary purpose of this research will be to detect, model, and predict ionospheric irregularities using a hybrid machine learning algorithm. In addition, using prediction values obtained from the proposed Hybrid models allow measuring the effect of ionospheric perturbations on GNSS ground-based precise positioning accuracy. This modeling and prediction algorithm can contribute to reducing the error of the ionospheric irregularities for satellite-based communication and navigation systems performance. For this purpose, near the equatorial ionization anomaly (EIA), GNSS ground-based stations in South America, are recommended since ionospheric disturbances most impact these regions. The proposed method can play a precaution role in alerting GNSS users that the observation epoch will be disturbed by ionospheric perturbations, and GNSS users can eliminate error-infected observations from the dataset.</p>
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