Accelerated progress in climate modeling is urgently needed for proactive and effective
climate change adaptation. The central challenge lies in accurately representing processes …

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 …

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 …

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 recent years, neural network models have been extensively applied in flood prediction
due to their superior performance. However, most studies aimed at enhancing models have …

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 …

Abstract We introduce CAMELS-IND (Catchment Attributes and MEteorology for Large-
sample Studies–India), a dataset containing hydrometeorological time series and catchment …

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