Prediction of Daily Stream-flow Using Data Driven Models

Document Type : Original Article

Authors

1 Assistant Professor, Department of Water Engineering, Gorgan University of Agricultural Sciences and Natural Resources., Gorgan., Iran

2 Associate Professor, Department of Water Engineering, Gorgan University of Agricultural Sciences and Natural Resources., Gorgan., Iran

3 Graduated Student of M.Sc. of Water Resources Engineering, Department of Water Engineering, Gorgan University of Agricultural Sciences and Natural Resources., Gorgan., Iran

Abstract

Accurate prediction of river daily discharge is a suitable tool for water resources planning and management. Using models that present explicit equation, such as M5 model trees and Genetic expression programming, causes increase efficiency of these models. In this study, the Galikesh basin as one of most flood prone basins in Gloestan Province is considered for the prediction of river daily discharge. Data series used in this study are long term 26 years daily rainfall and river discharge series belong to Galikesh meteorology and hydrometry station. Daily rainfall and river discharge data from 1 to 5 days ahead are used as inputs for prediction by M5 model trees, genetic expression programming and artificial neural network models. The results indicate very good efficiency of the investigated models beside overestimation of the models to predict daily river discharge. Comparison of results of different models leads to selection of M5 model trees as best model among investigated models. 

Keywords

References

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Iranian Journal of Irrigation & Drainage
Volume 10, Issue 4 - Serial Number 58
September and October 2016
Pages 479-488

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