• ann-som approach for satellite data pre-processing in rainfall-runoff modeling

    جزئیات بیشتر مقاله
    • تاریخ ارائه: 1390/11/15
    • تاریخ انتشار در تی پی بین: 1390/11/15
    • تعداد بازدید: 567
    • تعداد پرسش و پاسخ ها: 0
    • شماره تماس دبیرخانه رویداد: -
     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 satellite data 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.

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