Refractive Index Perception and Prediction of Radio wave through Recursive Neural Networks using Meteorological Data Parameters
Publish Year: 1401
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_IJE-35-4_032
تاریخ نمایه سازی: 10 اردیبهشت 1401
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 (۱۹۸۰ to ۲۰۲۰) metrological data obtained from the MERRA-۲ 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.
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Authors
S. Adebayo
Mechatronics Engineering Programme, College of Agriculture, Engineering, and Science, Bowen University, Iwo, Osun State, Nigeria
F. O. Aweda
Physics and Solar Energy Programme, College of Agriculture, Engineering, and Science, Bowen University, Iwo, Osun State, Nigeria
I. A. Ojedokun
Electrical and Electronics Department, Federal University, Otuoke, Nigeria
O. T. Olapade
Physics and Solar Energy Programme, College of Agriculture, Engineering, and Science, Bowen University, Iwo, Osun State, Nigeria
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