Refractive Index Perception and Prediction of Radio wave through Recursive Neural Networks using Meteorological Data Parameters

Abstract

Radio refractivity is very crucial in the optimal performance of radio systems and is one of the attributes that affect electromagnetic waves in the troposphere. This study presented a comparison of different variants of recurrent neural networks to predict radio refractivity index. The radio refractivity index is predicted based on forty-one years (1980 to 2020) metrological data obtained from the MERRA-2 data re-analysis database. The refractivity index was computed using International Telecommunication Union (ITU) standard. The correlation refractivity index was categorized into strong, weak and no correlation. Rainfall, relative humidity, and air pressure fall in the first category, the temperature falls in the second category while wind speed falls in the last one. The true future and predicted values of the radio refractivity index are close with GRU performing better than the other two models (LSTM and BiLSTM) which proves the accuracy of the proposed model. In conclusion, the proposed model can establish a radio refractivity status of locations at different times of the season, which is of great importance in the effective design, development, and deployment of radio communication systems.

Meteorological data prediction over selected stations in Sub-Sahara Africa: Leveraging on Machine Learning Algorithm

“…The data used in this study consist of monthly rainfall, air temperature, relative humidity, atmospheric pressure, wind speed and direction for five stations obtained from the archive of the HelioClim website of Soda (http://www.soda-pro.com) of MERRA-2 meteorological Re-Analysis data (Gelaro et al 2017, Aweda et al 2020a, Aweda et al 2020b, Aweda et al 2021a, Adebayo et al 2022. The data of forty-one years spanning from 1980 to 2020 were obtained as a monthly average from January to December of every year in Comma Separated Value (CSV) data format.…”

Performance Evaluation of Onboard Wi-Fi Module Antennas in Terms of Orientation and Position for IoT Applications

“…This is more true with indoor wireless networks. Even the small reflecting object can drastically affects the network's performance [4][5][6][7]. Among the various approaches to monitor and examine the surrounding environment, the most appropriate technique in recent years is wireless sensing [8].…”