This study investigates the ability of long short-term memory (LSTM) neural networks to
perform streamflow prediction at ungauged basins. A set of state-of-the-art, hydrological …

This paper presents an overview of scientific modeling and discusses the complementary
strengths and weaknesses of ML methods for scientific modeling in comparison to process …

Constrained by the sparsity of observational streamflow data, large-scale catchments face
pressing challenges in streamflow prediction and flood management amid climate change …

Abstract Machine learning (ML) has been broadly applied for vadose zone applications in
recent years. This article provides a comprehensive review of such developments. ML …

This study introduces a framework that strategically applies a Long Short-Term Memory
(LSTM)-based approach for monthly runoff prediction in South Africa and Central Asia. The …

Prediction of dynamic environmental variables in unmonitored sites remains a long-standing
challenge for water resources science. The majority of the world's freshwater resources have …

River runoff predictions in ungauged basins are one of the major challenges in hydrology. In
the past, the approach using a physical-based conceptual model was the main approach …

Hydrological modeling is a crucial component in hydrology research, particularly for
projecting future scenarios. However, achieving reproducibility and automation in distributed …

Previous studies have shown that regional deep learning (DL) models can improve runoff
prediction by leveraging large hydrological datasets. However, training a DL regional model …

The application of machine learning (ML) including deep learning models in hydrogeology
to model and predict groundwater level in monitoring wells has gained some traction in …