Abstract Machine learning (ML) has played an increasing role in the hydrological sciences.
In particular, Long Short-Term Memory (LSTM) networks are popular for rainfall–runoff …

Machine learning (ML) has played an increasing role in the hydrological sciences. In
particular, certain types of time series modeling strategies are popular for rainfall–runoff …

Machine learning (ML) applications in hydrology are revolutionizing our understanding and
prediction of hydrological processes, driven by advancements in artificial intelligence and …

The distribution of flowmeter data and basin characteristic information exhibits substantial
disparities, with most flow observations being recorded at a limited number of well …

In the 1990s, the constant error carousel and gating were introduced as the central ideas of
the Long Short-Term Memory (LSTM). Since then, LSTMs have stood the test of time and …

The forecast accuracy of machine learning (ML) weather prediction models is improving
rapidly, leading many to speak of a “second revolution in weather forecasting.” With …

Hybrid models that combine deep learning with physical principles have recently shown
significant promise in improving streamflow prediction in data-scarce regions, achieving …

Accurate and reliable hydrological forecasts play a pivotal role in ensuring water security,
facilitating flood preparedness, and supporting agriculture activities. This study investigates …

Abstract Study Region: Africa, with its diverse climatic zones from the humid Congo Basin to
the arid Sahara Desert, where groundwater is influenced by climate variability, land use, and …