Browsing by Author "Ewemooje, O. S."

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Best distribution and plotting positions of daily maximum flood estimation at Ona River in Ogun-Oshun River Basin, Nigeria
(International Commission of Agricultural and Biosystems Engineering, 2011) Ewemoje, T. A.; Ewemooje, O. S.
The paper discusses how Normal, Lognormal, and log-Pearson type 3 distributions were investigated as distributions for modelling at-site annual. maximum flood flows using the Hazen, Weibull, and California plotting positions at Ogun-Oshun river basin in Nigeria. All the probability distributions when matched with Weibull plotting position gave similar values near the center of the distribution but varied considerably in the tails. The Weibull plotting position when matched with Normal, Log-normal and Log Pearson Type III probability distributions gave the highest Coefficient of determinations of 0.967, 0.987, and 0.986 respectively. Hazen plotting position gave minimal errors with the RMSE of 6.988, 6.390, and 6.011 for Normal, Log-normal, and Log-Pearson Type III probability distributions respectively. This implies that, predicting statistically using Hazen plotting position, the central tendency of predicted values to deviate from observed flows will be minimal for the period under consideration. Minimum absolute differences of 2.3516 and 0.5763 at 25- and 50-year return periods' were obtained under the Log-Pearson Type III distribution when matched with Weibull plotting position, while an absolute difference of 0.2338 at 100-year return period was obtained under the Log-Pearson Type 1II distribution when matched with California .plotting position. Comparing the probability distributions, Log-Pearson Type III distribution with the least absolute differences for all the plotting positions is the best distribution among the three for Ona River under Ogun-Osun river basin study location.
Comparative evaluation of climatic data observation methods in Ibadan, Nigeria
(Kuwait University, 2010-06) Ewemoje, T. A.; Ewemooje, O. S.
Sustainability of agriculture is dependent upon availability of reliable climatic data for planning. Daily and monthly data were obtained for Ibadan, Nigeria from International Institute of Tropical Agriculture (IITA) automatic weather station and Nigerian Meteorological Agency (NMA) manual weather station Two methods were employed to analyse the data. First method (M1) used daily data to generate sets of linear equations. Each equation represents linear relationship between the climate parameter measured at station X (manual) and that same measured at station Y (automatic}. Second method (M2) used monthly values to analyse the data. Accuracy of regression method was analysed by calculating Error Variance (EV) between manual and automatic stations. Errors associated with deviation-based statistics (RMSE) are generally higher than regression-based statistics (EV) for all climate parameters considered. Introduced deviation and correlation based statistics of Mean Squared Deviation (MSD) and its components do not explicitly eliminate error introduced from linearity assumption in regression analysis. Hence, discrepancies have not been adequately explained, but with r < 0.50 in all the climate parameters, the model is weakly correlated with measurement. Analyses have shown that manually observed climate data should not be substituted with automatically observed climate data without correcting data bias/errors prior to usage
Hatchery production optimisation using Monte Carlo approach
(2012) Ewemoje, T. A.; Ewemooje, O. S.
Optimization of hatchery production processes was carried out using the Monte Carlo method. In the economics of engineering, decisions with the objective of the investor identifying an optimum solution. An investor chooses his or her optimal solution from the set of scenarios that offer maximum expected return for varying levels of risk. Outcomes associated with these random numbers are then analysed to determine the likely results and the associated risks. Taking a major day old hatchery as a case study, data were obtained from the daily production spreadsheet for a period of six years (2003-2008). Excel spreadsheet was used in simulating 6,631 iterations for each day old chick production quantity. Hatching 45000 fertile eggs always yields the largest expected profit when compared with the profit margin of hatching 5000, 15000, 25000 or 35000 fertile eggs. Therefore it appears as if hatching 45000 fertile eggs is the optimum production decision. Producing below the optimum production quantity, the mean profit obtained is very much lowered compared to the mean profit of the optimum 45000 production quantity. Also, production risks are higher below the optimum 45000 production quantity. This situation implies underutilization of the hatchery production system.