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

Deep learning techniques and algorithms are emerging as a disruptive technology with the
potential to transform global economies, environments and societies. They have been …

Understanding and predicting the quality of inland waters are challenging, particularly in the
context of intensifying climate extremes expected in the future. These challenges arise partly …

Recently, there has been a significant advancement in the water quality index (WQI) models
utilizing data-driven approaches, especially those integrating machine learning and artificial …

The increasing frequency and intensity of extreme climate events are among the most
expected and recognized consequences of climate change. Prediction of water quality …

Excellent water quality (WQ) is an indispensable element in ensuring sustainable water
resource development. It is highly associated with the 3rd (good health and well-being), the …

The sodium adsorption ratio (SAR) is the most crucial irrigation water quality indicator to
diagnose the suitability of agricultural water resources. Due to this reason, accurate …

This study presents a new approach to understand the causes of groundwater drought
events with interpretable deep learning (DL) models. As prerequisites, accurate long short …

Abstract Study region Tai Lake, the third largest freshwater lake in China, with a history of
serious ecological pollution incidents. Study focus Lake water quality prediction techniques …

The best practice of watershed management is through the understanding of the
hydrological processes. As a matter of fact, hydrological processes are highly associated …