Recently, there has been much attention in the use of machine learning methods,
particularly deep learning for stock price prediction. A major limitation of conventional deep …

Streamflow forecasting is the most well studied hydrological science but still portray
numerous unknown knowledge. The conventional physical-based model is unable to yield a …

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 …

Floods are governed by largely varying processes and thus exhibit various behaviors.
Classification of flood events into flood types and the determination of their respective …

Hydrodynamic models are useful tools for urban water management. Unfortunately, it is still
challenging to obtain accurate results and plausible uncertainty estimates when using these …

Hydrological models are typically calibrated with discharge time series derived from a rating
curve, which is subject to parametric and structural uncertainties that are usually neglected …

Runoff estimation is a key component in various hydrological considerations. Estimation of
storm runoff is especially important for the effective design of hydraulic and road structures …

Estimating the uncertainty of hydrological models remains a relevant challenge in applied
hydrology, mostly because it is not easy to parameterize the complex structure of …

There are no doubts that prediction of runoff is one of the crucial hydrological research
topics when dealing with catchments. It is noteworthy that the rapid land use change in …

In the last few decades tremendous progress has been made in the use of catchment
models for the analysis and understanding of hydrologic systems. A common application …