Recently, there has been a notable tendency towards employing ensemble learning
methodologies in assorted areas of engineering, such as hydrology, for simulation and …

Probabilistic forecasting is receiving growing attention nowadays in a variety of applied
fields, including hydrology. Several machine learning concepts and methods are notably …

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

Prediction of river flow rates is an essential task for both flood protection and optimal water
resource management. The high uncertainty associated with basin characteristics …

Predicting streamflows, which is crucial for flood defence and optimal management of water
resources for drinking, irrigation, hydropower generation and ecosystem conservation, is a …

In this study, it was investigated that how machine learning (ML) methods show performance
in different problems having different characteristics. Six ML approaches including Artificial …

Groundwater level (GWL) forecasting is crucial for irrigation scheduling, water supply and
land development. Machine learning (ML)(eg, artificial neural networks) has been …

Background: Open Access Editor's Choice Article A Stacking Ensemble Model of Various
Machine Learning Models for Daily Runoff Forecasting by Mingshen Lu 1, 2, Qinyao Hou 1 …

Lead (Pb) is a primary toxic heavy metal (HM) which present throughout the entire
ecosystem. Some commonly observed challenges in HM (Pb) prediction using artificial …

The hydrological cycle is an important process that controls how and where water is
distributed on Earth. It includes processes including transpiration, evaporation …