| Summary: | Ideal prediction and modeling of stream-flow and its hydrological applications are extremely significant for decision-making tasks and proper planning of water resource and hydraulic engineering. In the last two decades, the potential of soft computing approaches has increased dramatically in engineering and science problems. In this research, the utility of two soft computing approaches, namely support vector regression (SVR) model and generalized regression neural network (GRNN), is validated to predict 1 day ahead daily river flow data in the upper Senegal River basin at the Bafing Makana station in West Africa. The modeling is conducted by including the climatological information in the modeled stream-flow patterns. Correlation procedure is established and applied to obtain the modeling of the input variables with statistically significant lagged datasets at t − 1, t − 2, and t − 3 used as three input combination for each case study scenario. Different statistical indicators are used to evaluate the accuracy of the prediction models. The results show that the accuracy of the models varied by the scenario and the input datasets, where the SVR model yielded the best results for both modeling scenarios. It is also evident that combining the historical stream-flow data with the rainfall and evapotranspiration can ameliorate substantially the accuracy of the two models for predicting 1-day ahead stream-flow. A comparison of the optimal SVR and GRNN models in this problem indicates that SVR exhibits superior performance to the GRNN model in estimating the daily stream-flow data, irrespective of the modeling scenario and the datasets that is applied. The findings offer an opportunity to apply SVR model for predicting daily stream-flow, with less data requirement for the investigated Senegal River basin.
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