A Parsimonious Setup for Streamflow Forecasting using CNN-LSTM
This work addresses streamflow prediction for hydrology, but it is incremental as it applies an existing method to a new setting.
The study tackled streamflow forecasting by extending CNN-LSTM models from rainfall-runoff to time series settings using lagged streamflow, precipitation, and temperature data, resulting in improved predictive performance in 21 out of 32 basins with increased Kling-Gupta Efficiency values.
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 (KGE) 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.