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滑坡变形的多因素小波神经网络预测模型
Alternative TitleMultiple Factors Model for Landslide Deformation Prediction Based on Wavelet Neural Network
李秀珍; 王芳其
Corresponding Author李秀珍
2012
Source Publication水土保持通报
Volume32Issue:5Pages:235-238
Abstract小波神经网络模型结合了小波变换良好的时频局域化性质及传统神经网络具有的自学习功能,具有良好的逼近与容错能力。以某水电工程区的典型滑坡为例,在对滑坡的基本特征、滑坡变形与主要影响因素相关关系进行分析的基础上,选择滑坡位移速率和对滑坡位移起控制作用的降雨2个因素,建立了滑坡变形的小波神经网络预测模型,并与其他多因素小波神经网络模型进行了比较。结果表明,所建的滑坡多因素小波神经网络模型的预测精度总体均比较高,其中以位移速率和降雨量建立的2因素小波神经网络模型的预测精度最高,优于其他小波神经网络模型。
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
Language中文
CSCD IDCSCD:4694988
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
Identifierhttp://ir.imde.ac.cn/handle/131551/6579
Collection山地灾害与地表过程重点实验室
Recommended Citation
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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