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Applying radial basis function(RBF) neural network to predict the sediment deposited from check dam

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4 Author(s)
Wang Guozhong ; State Key Lab. of Water Resources & Hydropower Eng. Sci., Wuhan Univ., Wuhan, China ; Mei Yadong ; Shuang Rui ; Qu Jiangang

Three indicators (R, I30, P), and all four indicators (R, I30, P, I) of erosive rainfall in Jia Zhaichuan small watershed of Song county are chosen respectively as the input vector to predict sedimentation volume with the two neural network of RBF and BP, and fit with the actual values. The results testify the fitting and predicted effects of RBF neural network are all better than BP network, as well as the indexes (R, I30, P) are the main factors causing soil erosion.

Published in:

Computer Research and Development (ICCRD), 2011 3rd International Conference on  (Volume:3 )

Date of Conference:

11-13 March 2011