ANN-SOM approach for satellite data pre-processing in rainfall-runoff modeling

سال انتشار: 1391
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 1,925

فایل این مقاله در 9 صفحه با فرمت PDF قابل دریافت می باشد

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این مقاله:

شناسه ملی سند علمی:

ICCE09_445

تاریخ نمایه سازی: 7 مهر 1391

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.

کلیدواژه ها:

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

نویسندگان

Vahid Nourani,

Associate Prof., Department of Water Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

مراجع و منابع این مقاله:

لیست زیر مراجع و منابع استفاده شده در این مقاله را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود مقاله لینک شده اند :
  • Akhtar M. K., G. A. Corzo, S. J. van Andel1, ...
  • Antar, M.A., Elassiouti, I., Alam, M.N, and R ainfall-runof modeling ...
  • ASCE task committee on application of Artificial Neural Networks in ...
  • Dawson, C.W., Wilby, R., An artificial neural network approach to ...
  • Gavin J. Bowden, Graeme C. Dandy, Holger R. Maier, Input ...
  • Grimes, D.I.F., Coppola, E, Verdecchia, M., Visconti, G., A neural ...
  • Hornik, K., Multilayer feed-forward networks cre universal approximators, Neural Networks, ...
  • Hsu, K., Gupta, H.V., Sorooshian, S., Artificial neural network modeling ...
  • Hsu K. _ S. Li, Clustering sP atial-temporal precipitation data ...
  • Hsu, K., H. V. Gupta, X. Gao, S. Sorooshian, and ...
  • Joyce, R. J, J. E. Janowiak, P. A. Arkin, and ...
  • Kalteh, A.M., P. Hjorth and R. Berndtsson, Review of S ...
  • Kim, T., Valdes, J.B., Nonlinear model for drought forecasting based ...
  • Kohonen T., Self-organizing maps. Heidelberg: Springer- Verlag Berlin; 1997. ...
  • o" International Congress on Civi Engineering, May 8-10, 2012 Isfahan ...
  • Liu, Y., and Weisberg, R.H., A Review of S elf-Organizing ...
  • Nourani , V., Mano, A., S emi-dis tributed flood runoff ...
  • Nourani, V., Monadjemi, P., Singh, V.P., Liquid analog model for ...
  • Nourani V., and , Kalantari O., Integrated Artificial Neural Network ...
  • Nourani, V., Reply to comment _ Nourani V, Mogaddam A.A ...
  • _ V., Kisi 6., _ hybrid Artificial Intelligence approaches for ...
  • Salas, J.D., Delleur, J.W., Yevjevich, V., Lane, W.L, Applied Modeling ...
  • Senthil Kumar, A.R., Sudheer, K.P., Jain, S.K., Agarwal P.K., Rainfal ...
  • Toth E., Classification of hydro- meteorological conditions and multiple artificial ...
  • نمایش کامل مراجع