Stage -Discharge Relationship Modeling in Rivers Using Intelligent Systems

Document Type : Research Paper

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Abstract

Since continual measurement of rivers discharge, specialy at  flooding time is expensive and difficult task, modeling of stage-discharge relationship using mathematical and intelligent models have been  developed and used. In this research artificial neural networks, inference neuro-fuzzy and genetic programming were used for modeling daily stage- discharge relationship at the two stations, Yamula and Sogutluhan on the Kizilirmak  river in Turkey. Modeling were accomplished by the above three methods with various  combination of inputs  including the previous stage-discharge data. According to the results obtained,  neuro-fuzzy showed greater accuracy. Due to ability of genetic programming for evolving mathematical relationship and selecting significant variables, this method was selected as the best for stage-discharge relationship modeling  using four main operators including {+,-,*,/} with R2,RMSE and MAE for Yamula station are 1.000, 0.974 and 0.552 and for Sogutluhan station, 0.999, 1.095 and 0.630 respectively. 

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References

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