Over the past two decades, artificial neural networks (ANN) have exhibited a significant progress in predicting and modeling non-linear hydrological applications, such as the rainfall-runoff process which can provide useful contribution to water resources planning and management. This research aims to test the practicability of using ANNs with various input configurations to model the rainfall-runoff relationship in the Seybouse basin located in a semi-arid region in Algeria. Initially, the ANNs were developed for six sub-basins, and then for the complete watershed, considering four different input configurations. The 1st (ANN IP) considers only precipitation as an input variable for the daily flow simulation. The 2nd (ANN II) considers the 2nd variable in the model input with precipitation; it is one of the meteorological parameters (evapotranspiration, temperature, humidity, or wind speed). The third (ANN IIIP,T,HUM) considers a combination of temperature, humidity, and precipitation. The last (ANN VP,ET,T,HUM,Vw) consists in collating different meteorological parameters with precipitation as an input variable. ANN models are made for the whole basin with the same configurations as specified above. Better flow simulations were provided by (ANN IIP,T) and (ANN IIP,Vw) for the two stations of Medjez-Amar II and Bordj-Sabath, respectively. However, the (ANN VP,ET,T,HUM,Vw)’s application for the other stations and also for the entire basin reflects a strategy for the flow simulation and shows enhancement in the prediction accuracy over the other models studied. This has shown and confirmed that the more input variables, as more efficient the ANN model is.

MIKE SHE software was used to estimate recharge into the aquifers of Ogun and Oshun Basins. Abeokuta within the Ogun Basin and Oshogbo in the Oshun Basin are subdivided vertically into two components: atmosphere, and unsaturated zone. The atmosphere zone comprises of rainfall and potential evapotranspiration, while the unsaturated zones, comprises of the Basement Complex and Sedimentary rock. Daily records from two rainfall stations, Oshogbo station (2008–2011) and Abeokuta station (2010–2014) water years were obtained for simulation of groundwater recharge processes using MIKE SHE model. The simulation results showed that daily groundwater recharge is influenced by rainfall and ranges from 0 mm∙day–1 in January when there was an insufficient rainfall in the two stations to 10.89 mm∙day–1 in Abeokuta and 29.85 mm∙day–1 in Oshogbo in the month of August when the soils had attained field capacity. The study found out that there are more daily groundwater recharge in Oshun basin compared to that of Ogun basin. This was alluded to more rain-fall and less evapotranspiration recorded at Oshun basin as compared to Ogun basin coupled with the sedimentary soil which allows more movement of water into the aquifer of the basin. It is recommended MIKE SHE model should be used to estimate recharge in other basins in Nigeria and Africa for quick and effective daily recharge calculations to permit better and scientific decision making in these areas.