Iran-Water Resources Research

Iran-Water Resources Research

Peak Discharge forecast in the Downstream Station Using the Upstream Stations By Neural Network (Case Study: Taleghan)

Document Type : Technical Note (5 pages)

Authors
M Khosravi 1
A Salajegheh 2
1 M.Sc. graduate in Watershed Management, Faculty of Natural Resources, University of Tehran, Karaj, Iran
2 Assistant Professor, Faculty of Natural Resources, University of Tehran, Karaj, Iran.
Abstract
In cases that the gauging station in the downstream is destroyed for some reasons, and it is necessary to know the stream flow in the downstream, it is possible to forecast stream flow in the downstream station using the available data in the upstream station. In this research, the peak discharge of Gelinak station has been forecasted at outlet of the Taleghan watershed using artificial neural network in two states. In the first state, historic data of the Gelinak station including the maximum daily mean discharges, corresponding rainfall, one day antecedent rainfall and five days antecedent rainfall, sum of the five days antecedent rainfall and monthly mean temperature. In the second state,  these  data  for the hydrologic units of Gatehdeh, Mehran, Alizan, Joestan were extracted and the physiographic parameters area, average height, main waterway length, and the average river slope were added into the artificial Neural Network model. The model is feed forward with two layers and the back-propagation algorithm. Data were trained, validated, and tested in three stages. Results showed that the forecast of peak discharge using the upstream station and the physiographic parameters are better[A1]  than the peak discharge forecast using data from the last year in the downstream station



 
Keywords
Artificial Neural Network
Peak Discharge
Taleghan
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  • Receive Date 06 June 2011
  • Accept Date 26 February 2012