A Parsimonious Setup for Streamflow Forecasting using CNN-LSTM

Highlights A CNN-LSTM model was developed for time series forecasting of streamflow in Nebraska by combining CNN for spatial data and LSTM for sequence data. A substantial improvement was observed for 66% of the basins for this model compared to the standalone LSTM. This superior performance was achieved just by using gridded precipitation and 2-m temperature as exogenous inputs. 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.