The paper presents a comparison of lumped runoff modelling approaches, aimed at the realtime forecasting of flood events, based on or integrating Artificial Neural Networks (ANNs). ANNs are used in two ways: (a) as black-box type runoff simulation models or (b) for the real-time improvement of the discharge forecasts issued by a conceptual-type rainfall-runoff model. As far as the coupling of A...

a novel approach to determine optimal sampling locations under parameter uncertainty in a water distribution system (wds) for the purpose of its hydraulic model calibration is presented. the problem is formulated as a multi-objective optimisation problem under calibration parameter uncertainty. the objectives are to maximise the calibrated model accuracy and to minimise the number of sampling d...

In order to measure precipitation as the main variable for estimating the runoff and designing hydraulic structures, the satellite algorithm products that have the proper spatial and temporal coverage, can be used. In this study, at first, the daily streamflow simulation of Sarough-Cahy River from the Zarinehroud basin was conducted through the artificial neural network (ANN) and ground data of...

Modeling rainfall-runoff processes is a very challenging task due to data collection, time, money, and technology constraints. Artificial neural networks (ANNs) are modeling tools that can quickly adapt and learn input-output relationships for many different engineering problems. An Elman-type recurrent ANN was trained to simulate observed streamflow for Fountain Creek at Pueblo, CO, using vary...

Over the last decade or so, artificial neural networks (ANNs) have become one of the most promising tools for modelling hydrological processes such as rainfall runoff processes. However, the employment of a single model does not seem to be an appropriate approach for modelling such a complex, nonlinear, and discontinuous process that varies in space and time. For this reason, this study aims at...

Rainfall-Runoff is the most important hydrological variables used in most of the water resources applications. Watershed based planning and management requires thorough understanding hydrological process and accurate estimation of runoff. An Artificial Neural Network (ANN) methodology was employed to forecast daily runoff for the Kadam watershed of G-5 sub-basin of Godavari river basin. On the ...

runoff is one of the major components of calculating water resource processes and is the main issue in hydrology. many concept models are used to predict the amount of runoff, which in most cases depend on topographical and hydrological data. conventional models are not appropriate for areas in which there is little hydrological data. changes in runoff are nonlinear, meaning it is time & space ...

abstract land surface hydrological models has importance in the determination of soil moisture and temperature, the rate of evapotranspiration, stream flow by emphasis on the land surface physical and dynamic process descriptions. in this research, vic land surface hydrological model has been used for the land surface temperature and stream flow determination. the vic runoff simulation in each ...

This review considers the application of artificial neural networks (ANNs) to rainfall–runoff modelling and flood forecasting. This is an emerging field of research, characterized by a wide variety of techniques, a diversity of geographical contexts, a general absence of intermodel comparisons, and inconsistent reporting of model skill. This article begins by outlining the basic principles of A...