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

Author

Guozhong, Wang ; Yadong, Mei ; Rui, Shuang ; Jiangang, Qu

Author_Institution

State Key Lab. of Water Resources & Hydropower Eng. Sci., Wuhan Univ., Wuhan, China

Volume

3

fYear

2011

fDate

11-13 March 2011

Firstpage

181

Lastpage

183

Abstract

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.

Keywords

backpropagation; dams; erosion; geophysics computing; radial basis function networks; rain; sedimentation; sediments; BP neural network; Jia Zhaichuan small watershed; RBF neural network; Song county; check dam; erosive rainfall; radial basis function neural network; sediment deposit prediction; Artificial neural networks; Fitting; Indexes; Sediments; Soil; Training; Water conservation; BP; erosive rainfall; neural network; radial basis function (RBF); sediment deposited;

fLanguage

English

Publisher

ieee

Conference_Titel

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

Conference_Location

Shanghai

Print_ISBN

978-1-61284-839-6

Type

conf

DOI

10.1109/ICCRD.2011.5764274

Filename

5764274