Heat waves have become more frequent as global warming continues to raise the average temperature of the earth. This study investigated the annual average trend and variability of atmospheric temperature in Ijebu-Ode, Southwest Nigeria. Secondary data of atmospheric air temperature was obtained from the Nigeria Meteorological agency (NIMET) Ijebu-Ode, Ogun state station, which covers thirty-one (31) years. Both parametric (Least Square Regression) and non parametric (Mann Kendall) test was performed on the data to investigate the trends, while variability was investigated using the ttest statistics and standardized index. The analysis of result revealed that temporal air temperature trend has remained generally on the increase since 1983. The increase was gradual between 1991 and 2013. A slight drop in temperature was experienced between the late 1984 and 1985. Thereafter, the gradual increase continued until date. Both least square regression and Mann Kendall test showed that the increasing trend was significant. Stakeholders ranging from government, individuals and cooperate bodies have been encouraged to take the issue of climate variability serious in the study area and Southwest Nigeria in general.
The study developed multiple artificial neural network models with the aim of establishing the most suitable non-linear discharge perdition model of Ibu River. A 12-year daily discharge of River Ibu gauged near Sagamu was obtained from the Ogun-Oshun River Basin Development Authority (OORBDA), Abeokuta Nigeria to model and simulate daily discharge. The back-propagation method was used in developing the artificial neural network model. The study revealed that only three artificial neural network (ANN) models out of fifteen developed, have overall results that are satisfactory for prediction, out of these, the model with the least error was used for validation. The results obtained with ANNs based on two hidden layers for 1-day ahead are better than those obtained by models with single layers. It was concluded that the general performance of ANN models depends solely on the data used. While it was recommended that additional basin characteristics such as slope, geology, morphology and surface roughness features should be included to obtain more robust river discharge models.
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