Deep learning (DL) rainfall–runoff models outperform conceptual, process-based models in
a range of applications. However, it remains unclear whether DL models can produce …

Although deep learning models for stream temperature (Ts) have recently shown
exceptional accuracy, they have limited interpretability and cannot output untrained …

Soil moisture plays a crucial role in the hydrological cycle, but accurately predicting soil
moisture presents challenges due to the nonlinearity of soil water transport and variability of …

We investigate the applicability of machine learning technologies to the development of
parsimonious, interpretable, catchment‐scale hydrologic models using directed‐graph …

Recent advancements in deep learning (DL) have significantly improved hydrological
modeling by extracting generalities from large-sample datasets and enhancing predictive …

For a number of years since their introduction to hydrology, recurrent neural networks like
long short-term memory (LSTM) networks have proven remarkably difficult to surpass in …