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

Predictions and forecasts of machine learning models should take the form of probability
distributions, aiming to increase the quantity of information communicated to end users …

The machine learning algorithm 'random forest'has been applied in many areas of water
resources research including discharge simulation. Due to low setup and operation cost …

Daily streamflow forecasting through data-driven approaches is traditionally performed
using a single machine learning algorithm. Existing applications are mostly restricted to …

An accurate prediction of water quality (WQ) related parameters is considered as pivotal
decisive tool in sustainable water resources management. In this study, five different …

A novel ensemble-based conceptual-data-driven approach (CDDA) is developed where a
data-driven model (DDM) is used to “correct” the residuals from an ensemble of hydrological …

To improve streamflow predictions, researchers have implemented updating procedures that
correct predictions from a simulation model using machine learning methods, in which …

Deep neural network (DNN) models have become increasingly popular in the hydrology
community. However, most studies are related to (rainfall-) runoff simulation and …

We present a new method for simulating and predicting hydrologic variables with uncertainty
assessment and provide example applications to river flows. The method is identified with …

In recent decades, natural calamities such as drought and flood have caused widespread
economic and social damage. Climate change and rapid urbanization contribute to the …