Over the past few years, Deep Learning (DL) methods have garnered substantial
recognition within the field of hydrology and water resources applications. Beginning with a …

Process-based modelling offers interpretability and physical consistency in many domains of
geosciences but struggles to leverage large datasets efficiently. Machine-learning methods …

Recently deep learning (DL), as a new data‐driven technique compared to conventional
approaches, has attracted increasing attention in geophysical community, resulting in many …

Various forms of machine learning (ML) methods have historically played a valuable role in
environmental remote sensing research. With an increasing amount of “big data” from earth …

Deoxygenation is commonly observed in oceans and lakes but less expected in shallower,
flowing rivers. Here we reconstructed daily water temperature and dissolved oxygen in 580 …

Machine learning has emerged with big data technologies and high-performance computing
to create new opportunities for data intensive science in the multi-disciplinary agri …

Researchers have attempted to use machine learning algorithms to replace physically
based models for streamflow prediction. Although existing studies have contributed to …

Rainfall–runoff modelling is one of the key challenges in the field of hydrology. Various
approaches exist, ranging from physically based over conceptual to fully data-driven …

Despite the massive diversity in the modeling requirements for practical hydrological
applications, there remains a need to develop more reliable and intelligent expert systems …