This paper presents development of artificial neural network (ANN) models to compute received signal strength (RSS) for four VHF (very high frequency) broadcast stations using measured atmospheric parameters. The network was trained using Levenberg-Marquardt back-propagation (LMBP) algorithm. Evaluation of different effects of activation functions at the hidden and output layers, variation of number of neurons in the hidden layer and the use of different types of data normalisation were systematically applied in the training process. The mean and variance of calculated MSE (mean square error) for ten different iterations were compared for each network. From the results, the ANN model performed reasonably well as computed signal strength values had a good fit with the measured values. The computed MSE were very low with values ranging between 0.0027 and 0.0043. The accuracy of the trained model was tested on different datasets and it yielded good results with MSE of 0.0069 for one dataset and 0.0040 for another dataset. The measured field strength was also compared with ANN and ITU-R P. 526 diffraction models and a strong correlation was found to exist between the measured field strength and ANN computed signals, but no correlation existed between the measured field strength and the predicted field strength from diffraction model. ANN has thus proved to be a useful tool in computing signal strength based on atmospheric parameters.
An important characteristic of rainfall levels at a particular place is the statistical distribution of rainfall rate. In this paper, 1-minute, 5-minute and 30-minute integration time rainfall data were obtained from three different weather stations in Physics Department, Federal University of Technology, Minna, North Central Nigeria. The aim is to derive regression coefficients and conversion factors for predicting rainfall rate of 1-minute integration time from rainfall rates of other integration times. This was achieved by employing Segal, Flavin and Watson rain rate models. The results obtained revealed that there is a power law relationship between 1-minuterain rate (R1) and the equiprobable 5-minute rain rate (R5); and between 1-minute rain rate (R1) and the equiprobable 30-minute rain rate (R30). Also,it was observed that the values of regression coefficients a and b, and conversion factors Ce and CR derived in Minna were different from values derived at other locations in and outside Nigeria when comparisons were made. These discrepancies are attributed to the differences in climatic conditions between the regions among other factors. Therefore, these findings have further corroborated claims by earlier researchers that different regression coefficients and conversion factors are needed for different locations for 1-minute rain rate conversion.
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