Title :
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
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;
Conference_Titel :
Computer Research and Development (ICCRD), 2011 3rd International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-61284-839-6
DOI :
10.1109/ICCRD.2011.5764274
