Vahid Nourani, - Associate Prof., Department of Water Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran
Mohammad Taghi Aalami
Aida Hosseini Baghanam
Mekonnen Gebremichael

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.

Rainfall-runoff, wavelet, ANN, SOM, satellite data, pre-processing clustering- Gilgal Abay watershed