IMHE OpenIR  > 山地灾害与地表过程重点实验室
Alternative TitleMultiple Factors Model for Landslide Deformation Prediction Based on Wavelet Neural Network
李秀珍; 王芳其
Corresponding Author李秀珍
Source Publication水土保持通报
Other AbstractWavelet neural network has better approximation and fault-tolerance for combining the timefrequency localization of wavelet transform and self-study function of traditional neural network. We took some typical landslides in hydropower engineering region as an example and built three wavelet neural network models of multiple factors for landslide deformation prediction, on the basis of analyzing basic characteristics and the relationships between landslide deformation and main influencing factors of the landslide. By analyzing and comparing the results of the models, we found that the wavelet neural network model including the two factors (displacement rate and rainfall) has the highest prediction accuracy in the three models.
Keyword滑坡 变形预测 小波神经网络模型 多因素
Indexed ByCSCD
Funding Organization国家自然科学基金项目“基于小波分析的滑坡灾变预测方法研究”(40802072)
Citation statistics
Cited Times:1[CSCD]   [CSCD Record]
Document Type期刊论文
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GB/T 7714
李秀珍,王芳其. 滑坡变形的多因素小波神经网络预测模型[J]. 水土保持通报,2012,32(5):235-238.
APA 李秀珍,&王芳其.(2012).滑坡变形的多因素小波神经网络预测模型.水土保持通报,32(5),235-238.
MLA 李秀珍,et al."滑坡变形的多因素小波神经网络预测模型".水土保持通报 32.5(2012):235-238.
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