Accurate hydrological understanding and water cycle prediction are crucial for addressing
scientific and societal challenges associated with the management of water resources …

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 …

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

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

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

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

Abstract Purpose of Review Climate change has profoundly impacted the Earth's
atmospheric system and altered the terrestrial water cycle, reshaping the spatiotemporal …

Evaluating the soil moisture response characteristics to rainfall events is valuable for
understanding eco-hydrological effects in the context of global climate change. However, the …

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 …