Browsing by Author "Abdulkadir, T.S."
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Item Application of Artificial Neural Network Model to the Management of Hydropower Reservoirs along River Niger, Nigeria(Annals of Faculty Engineering Hunedoara – International Journal of Engineering, 2012) Abdulkadir, T.S.; Sule, B.F; Salami, A.W.Item Assessment of Neural Networks Performance in Modeling Rainfall Amounts(Department of Forestry, Wildlife and Range Management, University of Agriculture, Makurdi, Nigeria, 2017-03) Abdulkadir, T.S.; Salami, A.W; Aremu, A.S.; Ayanshola, A.M.; Oyejobi, D.O.This paper presents the evaluation of performance of Neural Network (NN) model in predicting the behavioral pattern of rainfall depths of some locations in the North Central zones of Nigeria. The input to the model is the consecutive rainfall depths data obtained from the Nigerian Meteorological (NiMET) Agency. The neural networks were trained using neural network toolbox in MATLAB with fifty years (1964–2014) total monthly historical data of five locations while two other locations, Abuja and Lafia with twenty-nine years (1986-2014) and eleven years (2004-2014) total monthly data respectively. Analysis showed the variation in the values of correlation coefficients (R) for each location of the study area in response to change in number of hidden neurons. The average R values of 0.80, 0.62, 0.65, 0.67, 0.79, 0.76 and 0.81 with corresponding mean square errors of 2.12, 0.23, 0.26, 0.36, 2.61, 1.18 and 1.03 were obtained for Abuja, Makurdi, Ilorin, Lokoja, Lafia, Minna and Jos respectively. The results showed some slight variability in the performances of NN due to changes in the number of hidden neurons during the network training. These values of R indicated that the networks are fit to be used for the subsequent quantitative prediction of rainfall depths in each location which is useful for safeguarding against future flood and drought occurrence in the North Central zone, Nigeria.Item Assessment of Quality Status of Soils around Dumpsites in Ilorin Metropolis, Nigeria.(Faculty of Engineering, University of Benin., 2020) Iji, J.O.; Mokuolu, O.A.; Abdulkadir, T.S.; Oluwaseun, O.V.Item Investigation of Rice Husk Ash Cementitious Constituent in Concrete(2014) Oyejobi, D.O.; Abdulkadir, T.S.; Ajibola, V.M.Item Probabilistic analysis of peak daily rainfall for prediction purposes in selected areas of northern Nigeria(Nigerian Journal of Technological Research, 2016) Salami, A.W.; Aremu, A.S.; Ayanshola, A.M.; Abdulkadir, T.S.; Garba, M.K.In this study, probability analysis was performed on peak daily rainfall data in order to predict rainfall interval values and to determine the best fit functions in some parts of Nigeria. The selected towns are Kaduna, Kano, Yola, Jos, Damaturu and Maiduguri. The obtained peak daily rainfall values were subjected to Gumbel, Log-Gumbel, Normal, Log-Normal, Pearson and Log-Pearson probability distributions. Mathematical equation for probability distribution functions were established for each town and used to predict peak rainfall. The predicted values were subjected to goodness of fit tests such as Chi-square, Correlation Coefficient, Coefficient of Determination and Errors of Estimates to determine how best the fits are. The model that satisfies the tests adequately was selected as the best fit model. The study revealed that the peak rainfall at Kaduna, Jos, Kano, Yola and Damaturu are best fitted by log-Gumbel, while log- Pearson distribution is suitable for predicting peak rainfall in Maiduguri. The result also shows that the occurrences of peak daily rainfall depth of 100 mm and above are rare in the selected areas.Item Probabilistic Analysis of Peak Daily Rainfall for Prediction Purposes in Selected Areas of Northern Nigeria(Nigerian Journal of Technological Research, 2016) Salami, A.W.; Aremu, A.S.; Ayanshola, A.M.; Abdulkadir, T.S.; Garba, M.K.