河北工业科技2017，Vol.34Issue(6)：440-447,8.DOI:10.7535/hbgykj.2017yx06009
基于回归-ELM神经网络模型的滑坡变形及失稳预测模型
Prediction model of landslide deformation and instability based on regression-ELM neural network model
翟会君 1翟亚锋 1朱涛 1炎杉杉1
作者信息
- 1. 河南省地质矿产勘查开发局第四地质勘查院,河南郑州 450001
- 折叠
摘要
Abstract
In order to predict the deformation trend of landslide accurately and prevent the occurrence of landslide effectively,a trend j udgment model based on deformation prediction and test is put forward.Firstly,regression analysis is used to fit the deformation curve of the landslide,and combined weights is used to achieve the combination of the fitting results,obtaining the preliminary results of landslide deformation prediction;secondly,extreme learning machine (ELM neural network)is used to correct the error of the initial forecast results,then the corrected results and the preliminary prediction results are processed together,so that the comprehensive prediction value of the landslide deformation is obtained;finally,the rank correlation coef-ficient test and Mann-Kendall test are used to estimate the trend of landslide deformation to verify the accuracy of the prediction result.The test shows that the prediction model is good,the combination forecasting and error correction both can improve prediction accuracy and stability in some degree,and the two model test results and the prediction results are consistent,which verifies each other's reliability.The prediction model can comprehensively j udge the trend of landslide deformation,which provides a new way for the study of landslide deformation.关键词
地基基础工程/滑坡/回归分析/极限学习机/秩相关系数检验/Mann-Kendall检验Key words
ground foundation engineering/landslide/regression analysis/extreme learning machine/rank correlation coeffi-cient test/Mann-Kendall test分类
天文与地球科学引用本文复制引用
翟会君,翟亚锋,朱涛,炎杉杉..基于回归-ELM神经网络模型的滑坡变形及失稳预测模型[J].河北工业科技,2017,34(6):440-447,8.
