The growing menace of global warming and restrictions on access to water in each region is
a huge threat to global hydrological sustainability. Hence, the perspective at which …

Water, one of the most valuable resources on Earth, is the subject of the study of hydrology,
which is of utmost importance. Satellite remote sensing (RS) has emerged as a critical tool …

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

Long short-term memory (LSTM) models are recurrent neural networks from the field of deep
learning (DL) which have shown promise for time series modelling, especially in conditions …

Accurate river streamflow forecasts are a vital tool in the fields of water security, flood
preparation and agriculture, as well as in industry more generally. Traditional physics-based …

Recently, the long short-term memory (LSTM) based rainfall-runoff models have achieved
good performance and thus have received many attentions. In this paper, we propose a …

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

We build three long short‐term memory (LSTM) daily streamflow prediction models (deep
learning networks) for 531 basins across the contiguous United States (CONUS), and …

Making accurate and reliable probability density forecasts of flood processes is
fundamentally challenging for machine learning techniques, especially when prediction …

Despite showing great success of applications in many commercial fields, machine learning
and data science models generally show limited success in many scientific fields, including …