Application of Neural Network in Atmospheric Refractivity Profile at Makurdi

DOI

10.2991/icibet.2013.107How to use a DOI?

Abstract

The refractivity profile variation in tropo-sphere is one of the aspects that influ-ences long-distance terrestrial electro-magnetic wave propagation and perfor-mance of communication systems. This study is aimed at calculating and estimat-ing radio refractivity at Makurdi with tropospheric parameters of relative hu-midity, absolute temperature and atmos-pheric pressure using ITU-R and artifi-cial neural network models. Validation results are thus, absolute temperature = 0.4313 K, relative humidity = 0.9989 %, pressure = 0.0201 (hpa) respectively. The validation of the correlation coefficient results shows that all the tropospheric pa-rameters have effects on radio re-fractivity, but relative humidity has more effect which is attributed to the large quantity of moisture at the troposphere. From the estimation results, it is clear that artificial neural network has the capacity of estimating tropopheric refractivity since the estimated values has close agreement with the calculated values.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - G.A Agbo
AU  - G. F Ibeh
AU  - D.U Onah
AU  - A.E Umahi
AU  - E Nnaji
AU  - F.C Ugwuonah
PY  - 2013/03
DA  - 2013/03
TI  - Application of Neural Network in Atmospheric Refractivity Profile at Makurdi
BT  - Proceedings of the 2013 International Conference on Information, Business and Education Technology (ICIBET 2013)
PB  - Atlantis Press
SP  - 508
EP  - 510
SN  - 1951-6851
UR  - https://doi.org/10.2991/icibet.2013.107
DO  - 10.2991/icibet.2013.107
ID  - Agbo2013/03
ER  -