Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation
Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation
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DOI：
10.1016/j.neucom.2015.08.118
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发表时间：
2016-07-19
期刊：
影响因子：
6
通讯作者：
Chen, Jiejie
中科院分区：
文献类型：
--
作者：
Jiang, Ping;Chen, Jiejie
本文提出了一种广义回归神经网络（GRNNS）与K折交叉验证（GRNNSK）方法预测滑坡位移。此外，相关分析是用来寻找潜在的输入变量，如皮尔逊互相关系数（PCC）和互信息（MI），本文中应用。通过对三峡库区凉水井滑坡和白水河滑坡的实例分析，验证了该方法的有效性。(C)© 2016 Elsevier B.V.版权所有。
In this paper, we propose a generalized regression neural networks (GRNNS) with K-fold cross-validation (GRNNSK) method for predicting the displacement of landslide. Furthermore, correlation analysis is used to find the potential input variables for this predicting model, such as Pearson cross-correlation coefficients (PCC) and mutual information (MI) are applied in this paper. Tests on two case studies of Liangshuijing (LSJ) and Baishuihe (BSH) landslide in the Three Gorges reservoir area of China demonstrate the effectiveness of the proposed approach. (C) 2016 Elsevier B.V. All rights reserved.