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

Streamflow prediction (SFP) constitutes a fundamental basis for reliable drought and flood
forecasting, optimal reservoir management, and equitable water allocation. Despite …

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

Alpine basins are important water sources for human life, and reliable hydrological modeling
can enhance the water resource management in alpine basins. Recently, hybrid …

Recently, differentiable modeling techniques have emerged as a promising approach to
bidirectionally integrating neural networks and hydrologic models, achieving performance …

Neural network models have been widely used in runoff forecasting, but are often criticized
for their lack of physical interpretability. In this study, we present a simple but useful …

A deep learning model designed for time series predictions, the long short-term memory
(LSTM) architecture, is regularly producing reliable results in local and regional rainfall …

Understanding ancient organisms and their interactions with paleoenvironments through the
study of body fossils is a central tenet of paleontology. Advances in digital image capture …

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

Although deep learning (DL) models, especially long-short-term memory (LSTM),
demonstrate greater accuracy than process-based models in rainfall-runoff simulation, the …