چکیده مقاله
In this paper, the application of artificial neural networks ANN to optimise the results obtained from a hydrodynamic model of river flow was evaluated The study area is Reynolds Creek Experimental Watershed in southwest Idaho, USA A hydrodynamic model was constructed to predict flow at theoutlet using time series data from upstream gauging sites as boundary conditions In the second stage, the model was replaced with an ANN model but with the same inputs Finally the error of the hydrodynamic model was predicted using an ANN model to optimise the outputs Simulations were carried out for two different conditions with and without data from a recently suspended gauging site to evaluate the effect of this suspension in hydrodynamic, ANN and the combined model Using ANN in this way, the error produced by the hydrodynamic model is predicted and thereby, the results of the model are improved In addition, the results of hydrodynamic modelling affected by the suspension of the flow gauging is appropriately improved by neural networks Combination of these two techniques for this specific application uses the potential of both methods and shows a good performance
کلیدواژهها
نویسندگان
Nigel G. Wright
School of Civil engineering, University of Nottingham, Nottingham NG۷ ۲RD, UK
Mohammad T Dastorani
School of Civil engineering, University of Nottingham, Nottingham NG۷ ۲RD, UK
شیوه ارجاع
Nigel G. Wright, and Dastorani, Mohammad T,1382,Artificial neural networks as a corrector of hydrodynamic modelling results,6th International Conference on Civil Engineering,Isfahan
ارائهشده در
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