Predictions of hydrologic variables across the entire water cycle have significant value for
water resources management as well as downstream applications such as ecosystem and …

The critical zone (CZ) includes natural and anthropogenic environments, where life, energy
and matter cycles combine in complex interactions in time and space. Critical zone …

The overexploitation of groundwater resource and its delicacy management has gained
increasing attentions in recent years worldwide because of causing a series of serious …

Data-driven models (DDMs) have gained increasing popularity in groundwater hydrology in
recent years due to the advancement of machine learning algorithms and the flexibility of …

Abstract Machine learning methods provide new perspective for more convenient and
efficient prediction of groundwater flow. In this study, a deep learning method “GW-PINN” …

We present an approach that uses a deep learning model, in particular, a MultiLayer
Perceptron, for estimating the missing values of a variable in multivariate time series data …

In this study, a deep learning model is proposed to predict groundwater levels. The model is
able to accurately complete the prediction task even when the data utilized are insufficient …

Traditionally, water quality is assessed using costly laboratory and statistical methods,
rendering real‐time monitoring useless. Poor water quality requires a more practical and …

Abstract Current implementations of Physics Informed Neural Networks (PINNs) can
experience convergence problems in simulating fluid flow in porous media with highly …

Precise estimation of groundwater level (GWL) might be of great importance for attaining
sustainable development goals and integrated water resources management. Compared …