Solid wastes have varied compositions and constituents from place to place. The study area is not an excep- tion. The entire 5 Local Government Area (LGA)s of Ogbomosoland were surveyed and about 40 major dumpsites were identified across the spread. Twenty-five (25) of these were selected, five (5) each per LGA, for the study. The wastes were collected from the dumps, sorted, weighed and classified according to their constituents. The densities of wastes from the 25 dumpsites were also determined. The overall average composition using the main classes of wastes were found to be food; 68.4%, metals; 7.2%, textile; 4.6%, papers; 4.4%, plastic; 3.9%, glass; 3.6%, wood; 3.1%, and miscellaneous; 4.8%. The average waste density for the study area was 438.1 kg/m3. Putrescible materials dominated the waste composition of the study area. The components of wastes in the city revealed a higher standard of living when compared with those of the residents in the environs. Rural residents generate denser wastes when compared with the urban centres and as such are prone to leachate pollution emanating from these organic wastes. The ingress of leachate is a threat to the groundwater resources of the study area
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.