A parsimonious setup for streamflow forecasting using CNN-LSTM

Abstract

Significant strides have been made in advancing streamflow predictions, notably with the introduction of cutting-edge machine-learning models. Predominantly, long short-term memories (LSTMs) and convolution neural networks (CNNs) have been widely employed in this domain. While LSTMs are applicable in both rainfall-runoff and time-series settings, CNN–LSTMs have primarily been utilized in rainfall-runoff scenarios. In this study, we extend the application of CNN–LSTMs to time-series settings, leveraging lagged streamflow data in conjunction with precipitation and temperature data to predict streamflow. Our results show a substantial improvement in predictive performance in 21 out of 32 HUC8 basins in Nebraska, showcasing noteworthy increases in the Kling–Gupta efficiency values. These results highlight the effectiveness of CNN–LSTMs in time-series settings, particularly for spatiotemporal hydrological modeling, for more accurate and robust streamflow predictions.