Performance of a Virtual Runoff Hydrograph System

Carriere, Patrick; Mohaghegh, Shahab D.; Gaskari, Razi

doi:10.1061/(asce)0733-9496(1996)122:6(421)

Cited by 43 publications

(19 citation statements)

References 3 publications

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“…Carriere et al ( 1996) developed a virtual runoff hydrograph system that employed a recurrent back propagation ANN to generate a runoff hydrograph. Shamseldin et al (1997) used the conjugate gradient method to train the feed forward network using daily average rainfall and runoff data from six catchments from different climatic conditions around the world.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Soft Computing Tools in Rainfall-Runoff Modeling

Jothiprakash¹,

M.Ish²

2009

ISH Journal of Hydraulic Engineering

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The use of rainfall-runoff models in the decision making process of water resources planning and management has become increasingly indispensable. Rainfall-runoff modeling in the broad sense started at the end of l9 1 h century and till today there are various types of models based on their mechanism, input data and other modeling requirements. These type of models range from physical, conceptual, empirical models and more sophisticated models like Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS), Genetic Programming (GP), Model Tree (MT), Support Vector Machine (SVM) and recently Chaos theory. The primary aim of this paper is to review the recent works on Rainfall-Runoff modeling using soft computing techniques.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Soft Computing Tools in Rainfall-Runoff Modeling

Jothiprakash¹,

M.Ish²

2009

ISH Journal of Hydraulic Engineering

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“…Muttiah et al (1997) used information on the drainage basin, elevation, average slope, and average annual precipitation to predict 2-year peak discharge from a watershed. Carriere et al (1996) used ANN with a recurrent back-propagation algorithm to generate a runoff hydrograph using a virtual runoff hydrograph system. They used rainfall intensity, duration, catchment slope, and catchment cover to estimate runoff hydrographs.…”

Section: Application Of Ann In Hydrologymentioning

confidence: 99%

An artificial neural network model for generating hydrograph from hydro-meteorological parameters

Ahmad

Simonović́

2005

Journal of Hydrology

106

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“…Smith [9] used a back-propagation ANN model to predict the peak discharge and the time to peak resulting from a single rainfall pattern. Carriere et al [10] designed and developed a Virtual Runoff Hydrograph System (VROHS) based on ANN to generate runoff hydrograph. Lange [11] introduced a method which generates a special hydrograph (comparable with the unit hydrograph) by using an ANN.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks for Event Based Rainfall-Runoff Modeling

Sarkar¹,

Kumar²

2012

JWARP

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The Artificial Neural Network (ANN) approach has been successfully used in many hydrological studies especially the rainfall-runoff modeling using continuous data. The present study examines its applicability to model the event-based rainfall-runoff process. A case study has been done for Ajay river basin to develop event-based rainfall-runoff model for the basin to simulate the hourly runoff at Sarath gauging site. The results demonstrate that ANN models are able to provide a good representation of an event-based rainfall-runoff process. The two important parameters, when predicting a flood hydrograph, are the magnitude of the peak discharge and the time to peak discharge. The developed ANN models have been able to predict this information with great accuracy. This shows that ANNs can be very efficient in modeling an event-based rainfall-runoff process for determining the peak discharge and time to the peak discharge very accurately. This is important in water resources design and management applications, where peak discharge and time to peak discharge are important input variables.

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Performance of a Virtual Runoff Hydrograph System

Cited by 43 publications

References 3 publications

Soft Computing Tools in Rainfall-Runoff Modeling

Soft Computing Tools in Rainfall-Runoff Modeling

An artificial neural network model for generating hydrograph from hydro-meteorological parameters

Artificial Neural Networks for Event Based Rainfall-Runoff Modeling

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