Multi-Output Neural Networks for Estimation of Synthetic Unit Hydrograph Parameters: A Case Study of a Catchment in Turkey

• Authors: A. Güven , A. Günal , M. Günal
• Languages of publication: EN

Abstracts

EN

For developing unit hydrographs of catchments, the detailed information about the rainfall and the resulting flood hydrographs are needed. Such information, however, is available only for a few locations and for the remote locations such information is normally very scanty. In this study, Snyder based synthetic unit hydrographs were developed by using both, the digitized map and the digital elevation model of a case study of a small catchment in Turkey. Multi-output neural network technique was applied to predict three unit hydrograph parameters: peak discharge q_{p}, time to peak t_{p} and time base t_{b} of a number of unit hydrographs observed in the catchment, based on most relevant geomorphological and meteorological parameters. Multi-output neural network was observed to outperform the conventional synthetic unit hydrograph methods. The advantage of the proposed multi-output neural network is based on the fact that it predicts the three parameters of the unit hydrograph, based on a single model, compared to the conventional neural network technique, which utilizes a model for each parameter.

Keywords

EN

92.40.FB; 92.40.QP; 07.05.MH

• Publisher: Institute of Physics, Polish Academy of Sciences
• Journal: Acta Physica Polonica A
• Year: 2017
• Volume: 132
• Issue: 3
• Pages: 591-594

Dates

published

2017-09

Contributors

author

A. Güven

• Uviversity of Gaziantep, Department of Civil Engineering, Gaziantep, Turkey

author

A. Günal

• Uviversity of Gaziantep, Department of Civil Engineering, Gaziantep, Turkey

author

M. Günal

• Uviversity of Gaziantep, Department of Civil Engineering, Gaziantep, Turkey

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Identifiers

DOI

10.12693/APhysPolA.132.591