Bio
Dr. Ibeh Gabriel is a dedicated researcher and academic affiliated with the Department of Industrial Physics at Ebonyi State University, Abakaliki, Nigeria. With a strong background in atmospheric physics, Dr. Gabriel holds an M.Sc and Ph.D. in the field. His research interests span atmospheric physics, artificial intelligence applications in environmental modeling, and solar energy estimation. He has authored notable works including 'Estimation Of Tropospheric Refractivity With Artificial Neural Network At Minna, Nigeria' and 'Comparison of Angstrom-Prescott, Multiple Regression and Artificial Neural Network Models for the Estimation of Global Solar Radiation in Warri, Nigeria'. Dr. Gabriel has contributed as a reviewer for the Global Journal of Computer Science and Technology and has supervised multiple research projects and scholars. His work reflects a commitment to advancing knowledge in atmospheric and solar sciences within the Nigerian context.
Educational Journey
M.Sc, Ph.D.
Experience
Research
Comparison of Angstrom-Prescott, Multiple Regression and Artificial Neural Network Models for the Estimation of Global Solar Radiation in Warri, Nigeria
In this paper, the application of artificial neural network, Angstrom-Prescott and multiple regressions models to study the estimation of global solar radiation in Warri, Nigeria for a time period of seventeen years were carried out. Our study based on Multi-Layer Perceptron (MLP) of artificial neural network was trained and tested using seventeen years (1991-2007) meteorological data. The error results and statistical analysis shows that MLP network has the minimum forecasting error and can be considered as a better model to estimate global solar radiation in Warri compare to the estimation from multiple regressions and Angstrom-Prescott models.
Estimation Of Tropospheric Refractivity With Artificial Neural Network At Minna, Nigeria
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.
