Prediction of Landslide Volume Based on Quantitative Theory and BP Neural Network

HUANG Zhiquan; MENG Lingchao; HUANG Xiangchun; WANG Wei

doi:10.13961/j.cnki.stbctb.2016.05.040

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Prediction of Landslide Volume Based on Quantitative Theory and BP Neural Network

- Prediction of Landslide Volume Based on Quantitative Theory and BP Neural Network
- Bulletin of Soiland Water Conservation Vol. 36, Issue 5, Pages: 207-213(2016)
- 作者机构：
  
  1. 新疆工程学院,新疆,乌鲁木齐,830091
  2. 华北水利水电大学资源与环境学院,河南,郑州,450011
- 作者简介：
- 基金信息：
- DOI：10.13961/j.cnki.stbctb.2016.05.040
  CLC：
- Published：2016
- 稿件说明：
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HUANG Zhiquan, MENG Lingchao, HUANG Xiangchun, et al. Prediction of Landslide Volume Based on Quantitative Theory and BP Neural Network[J]. Bulletin of Soiland Water Conservation, 2016, 36(5): 207-213.
DOI：

HUANG Zhiquan, MENG Lingchao, HUANG Xiangchun, et al. Prediction of Landslide Volume Based on Quantitative Theory and BP Neural Network[J]. Bulletin of Soiland Water Conservation, 2016, 36(5): 207-213. DOI： 10.13961/j.cnki.stbctb.2016.05.040.

摘要

[目的] 探讨数量化理论Ⅲ和BP神经网络在滑坡中综合应用的效果，为滑坡体积的预测提供一种新的思路。[方法] 采用数量化理论Ⅲ分析滑坡体积的影响因素及其耦合作用强度，并结合其分析结果，将次要因素和强耦合程度样本进行剔除，再依据其剔除的不同阶段构建3种滑坡体积的BP神经网络预测模型。[结果] 滑坡体积的主要影响因素是坡角、坡向、植被覆盖率和坡高，次要影响因素是岩层倾角、斜坡高程和岩层倾向因素，且在不同样本中，体积影响因素之间的耦合程度具有一定的差异。[结论] 该预测方法可行，对次要因素和强耦合程度样本的剔除，提高了预测精度。

Abstract

[Objective] The objective of this study is to explore the effect of the comprehensive application of the third theory of quantification and BP neural network in the landslide

in order to provide a new method for the prediction of landslide volume.[Methods] The influence factors of landslide volume and its coupling strength were analyzed by the third theory of quantitatification. Based on the analysis results

the secondary factors and strong coupling degree samples were removed

and then the BP neural network prediction models of 3 different kinds of landslide volume was built according to different stages of the elimination.[Results] The main influencing factors of landslide volume were slope angle

slope

vegetation coverage rate and slope high

while the secondary influence factors were the dip angle

elevation and slope rock orientation. And in different samples

the degree of coupling between the volume influencing factors was difference.[Conclusion] The prediction method used in the present study is feasible

and the prediction accuracy can be improved by eliminating the secondary factors and the strong coupling degree samples.

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