Accurate Prediction of Streamflow Using Long Short-Term Memory Network: A Case Study in the Brazos River Basin in Texas

Abstract

Accurate prediction of streamflow plays a pivotal role for effective reservoir system operations. Specifically, streamflow forecasting provides valuable information for reservoir operators to make critical decisions on water release amount to maximize reservoir storage benefits considering tradeoffs among flood control, municipal water supply, irrigation, hydropower etc. This task, however, has posed daunting challenges due to the complex mechanisms of the physical-based processes as well as the influence of uncontrollable factors. Hence, developing a robust mathematically driven model -in tandem with the supervision of proficient hydrologists for validation purposes -to ensure an accurate forecasting of discharge flows could be of paramount importance. To this end, a deep learning framework using a variation of recurrent neural networks called Long Short-term Memory (LSTM) network, for an accurate prediction of streamflow is presented and evaluated -without losing any generality -for a watershed outlet at the United States Geological Survey (USGS) gauge station neighboring Hempstead within the poorly-gauged region of Brazos basin in Texas with temporal coverage of 2007-2010. In this work, the antecedent precipitation observations and the climate variability indices have been utilized as the potential predictors. Our model is, however, scalable and transferable to be deployed across variant basins with various drainage areas. We, herein, assessed the performance of our predictive model via the Pearson correlation ( ) and the Nash-Sutcliffe model efficiency (NSE) coefficients between the predicted and observed streamflow, achieving and NSE of 0.9542 and 0.8859, respectively.