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 …

Artificial intelligence has undergone rapid development in the last thirty years and has been
widely used in the fields of materials, new energy, medicine, and engineering. Similarly, a …

The management of water resources depends heavily on hydrological prediction, and
advances in machine learning (ML) present prospects for improving predictive modelling …

There exists a substantial disparity in the distribution of streamflow gauge and basin
characteristic information, with a majority of flood observations being recorded from a limited …

Abstract Interpretable Machine Learning (IML) has rapidly advanced in recent years, offering
new opportunities to improve our understanding of the complex Earth system. IML goes …

Streamflow and flood forecasting remains one of the long-standing challenges in hydrology.
Traditional physically based models are hampered by sparse parameters and complex …

The runoff prediction can provide scientific basis for flood control, disaster reduction and
water resources planning. Due to a large number of uncertainties in runoff prediction, it is …

While deep learning (DL) models exhibit superior simulation accuracy over traditional
distributed hydrological models (DHMs), their main limitations lie in opacity and the absence …

Abstract Model development in groundwater simulation and physics informed deep learning
(DL) has been advancing separately with limited integration. This study develops a general …

Long short-term memory (LSTM) networks have demonstrated their excellent capability in
processing long-length temporal dynamics and have proven to be effective in precipitation …