Rainfall-runoff modeling via artificial neural networks for simulation of streamflows from a hydrographic basin in the Amazon
Abstract
The techniques of artificial neural networks (ANNs) have been used in the prediction of hydrological variables due to the ability to generalize information, which makes the implementation of models less costly and more agile. In this study, the phenomenon of converting rainfall into streamflows of a Guamá River Hydrographic Sub-basin (GRHS) in the State of Pará, Amazon, was simulated. The models are based on MLP (Multilayer Perceptron) and NARX (Nonlinear Autoregressive with Exogenous Inputs) ANNs, with Hyperbolic Tangent activation function in hidden layer neurons, being trained by the supervised training algorithm Levenberg-Marquardt. Comparing the proposed ANNs, the NARX-ANN models presented better performances compared to the MLP-ANN model. The best of the NARX-ANN models presented, on average, for the training, validation and test phases, R² equal to 0.9901, RMSE equal to 11.73 m3s-1 and MAPE equal to 5.94%. These results show the possibility of simulating the streamflow of small and medium hydrographic basins in the Amazon through the combination of NARX-ANNs, mainly those basins with no or limited rainfall-flow data.
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