Estimation Of Tropospheric Refractivity With Artificial Neural Network At Minna, Nigeria (Global Journal of Science Frontier Research, 2012)
The study of refractivity and its effect at the tropospheric region is very important as the parameters help in planning for communication links. This study is aimed at calculating and estimation of refractivity at the tropospheric region with tropospheric parameters of relative humidity, absolute temperature and atmospheric pressure of January and October at Minna, Nigeria. The ITU-R, model and artificial neural network model were used. Validation results are thus, January, absolute temperature = 0.4313 K, relative humidity = 0.9989 %, pressure = 0.0201 (hpa) and October, absolute temperature = -0.3146 K, relative humidity = 0.9597 % and pressure = 0.1962 respectively. The validation of the correlation coefficient results show that all the tropospheric parameters has effects on refractivity, but relative humidity has more effect and is merely on October which was attributed to the large quantity of moisture at the tropospheric region during the rainy season which is between April to October as stated by Adadiji. From Table 1 and 2 and figure 1 to 6, it clear that ANN has the capacity of estimating refractivity since the estimated values has close agreement with the calculated values.
Keywords: Troposphere; refractivity; artificial neural network; atmosphere