Tools from the field of deep learning are being used more widely in hydrological science.
The potential of these methods lies in the ability to generate interpretable and physically
realistic forecasts directly from data, by utilising specific neural network architectures. This
approach offers two advantages which complement physically-based models. First, the
interpretations can be checked against our physical understanding to ensure that where
deep learning models produce accurate forecasts they do so for physically-defensible …