RESUMO
Reliable and accurate modelling of streamflow is still a challenging task due to their complex behaviour, need for extensive parameter for development as well as lack of complete or accurate data. In this study, the applicability of an emerging data-driven model, specifically a neural network autoregression (NNAR) model, was evaluated for the first time as a substitute to the physically based hydrological model Soil and Water Assessment Tool (SWAT) for predicting streamflow under data-scarce conditions and for immediate high-quality modelling results. The inputs to the NNAR model were the lagged values of the daily streamflow time series data, and the output was the predicted value for the next day. Using streamflow data that was windowed by 20 days, the NNAR model produced the best prediction. The results of the statistical metrics used to evaluate the performance of the NNAR model were satisfactory (R = 0.90, RMSE = 28.27, MAE = 11.92, R2 = 0.83), indicating a high degree of agreement between the predicted and observed streamflow. The NNAR model outputs demonstrated its ability to accurately predict streamflow in the river basin, even without an explicit understanding of the physical processes that govern the system.
