چکیده مقاله
The use of artificial neural network ANN models in water resource applications as rainfall runoff modeling has grown considerably over the last decade In order to obtain more accurate models, the qualification of applied data must be improved Satellite data as a source of proper data in field of rainfall measurement over a watershed is utilized in this paper Doubtlessly, spatial pre processing methods can promote the quality of precipitation data In the current research the self organizing map SOM is used for spatial pre processing purpose A two level SOM neural network is applied to identify spatially homogeneous clusters of the satellitedata in order to choose the most operative and effective data for the Feed Forward Neural Network FFNN model which is trained by the Levenberg Marquardt algorithm and considering only one hidden layer The results indicate that the imposition of spatial pre processed data to the FFNN model lead to promising evidence in the improvement of rainfall runoff model
کلیدواژهها
نویسندگان
وحید نورانی
Associate Prof , Department of Water Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran
Mohammad Taghi Aalami
Vahid Nourani Associate Prof., Department of Water Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, IranAida Hosseini BaghanamMekonnen Gebremichael
Aida Hosseini Baghanam
Vahid Nourani Associate Prof., Department of Water Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, IranMohammad Taghi AalamiMekonnen Gebremichael
Mekonnen Gebremichael
Vahid Nourani Associate Prof., Department of Water Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, IranMohammad Taghi AalamiAida Hosseini Baghanam
شیوه ارجاع
Nourani, Vahid and Aalami, Mohammad Taghi and Hosseini Baghanam, Aida and Gebremichael, Mekonnen,1391,ANN-SOM approach for satellite data pre-processing in rainfall-runoff modeling,9th International Congress on Civil Engineering,Isfahan
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