Despite the considerable success of deep learning methods in modelling physical
processes, they suffer from a variety of issues such as overfitting and lack of interpretability …

The infrastructure design is primarily based on rainfall intensity–duration–frequency (IDF)
curves, and the existing IDF curves are based on the concept of stationary extreme value …

A daily rainfall-runoff model has been improved by the integration of artificial neural network
(ANN) and genetic algorithm (GA). The integrations are carried out on the daily rainfall-runoff …

An evaluation of the Sacramento Soil Moisture Accounting (SAC-SMA) model was
conducted to be used in flood event simulations with datasets at a time step up to one hour …

This study proposes a risk assessment framework for quantifying the reliability of the rainfall
threshold used in flash flood warning, which should be influenced by the uncertainties in the …

Global optimization methods linked with simulation models are widely used for automated
calibration and serve as useful tools for searching for cost-effective alternatives for …

The effective representation of land surface hydrological models strongly relies on spatially
varying parameters that require calibration. Well-calibrated physical models can effectively …

This study aims to develop a smart model for carrying out two-dimensional (2D) inundation
simulation by estimating the gridded inundation depths via the ANN-derived models …

The calibration of an event based rainfall-runoff model for steam flow forecasting is
challenging because, it is difficult to measure the parameters physically on the field for each …

This study proposes a stochastic artificial neural network (named ANN_GA-SA_MTF), in
which the parameters of the multiple transfer functions considered are calibrated by the …