|Table of Contents|

Improved Method for Fusion of Loess Landslide Monitoring Data Based on Feature Selection and Stepwise Regression(PDF)

《地球科学与环境学报》[ISSN:1672-6561/CN:61-1423/P]

Issue:
2023年第03期
Page:
511-521
Research Field:
大地测量、遥感与地学大数据
Publishing date:

Info

Title:
Improved Method for Fusion of Loess Landslide Monitoring Data Based on Feature Selection and Stepwise Regression
Author(s):
WANG Li1; 2; 3;  ZHANG Yi-kai1; 2; 3;  SHU Bao1; 2; 3*;  XU Hao1; 2; 3;  WEI Tuo1; 2; 3;  LEI Ti-jun1; 2; 3
(1. School of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, Shaanxi, China; 2. State Key Laboratory of Geo-information Engineering, Xi'an 710054, Shaanxi, China; 3. Key Laboratory of Western China's Mineral Resources and Geological Engineering of Ministry of Education, Chang'an University, Xi'an 710054, Shaanxi, China)
Keywords:
loess landslide;  deformation monitoring;  multi-source data fusion;  multi-point monitoring;  weighted correlation degree;  feature selection;  stepwise regression;  Heifangtai
PACS:
P228; P642.22
DOI:
10.19814/j.jese.2022.09064
Abstract:
Aiming at the problems of difficult screening of influencing factors, large difference of results and high complexity of data processing in multi-source heterogeneous data fusion processing of landslide monitoring, a multi-source and multi-point heterogeneous monitoring data fusion method of loess landslide based on maximal information coefficient(MIC), grey relational analysis(GRA)and stepwise regression was proposed. Firstly, the maximum mutual information coefficient and grey correlation analysis are combined, and the feature optimization method based on weighted correlation degree is used to comprehensively screen the influencing factors of landslide deformation, extract representative features and eliminate factors with poor correlation. Secondly, the importance weight coefficient corresponding to the displacement of each monitoring point and the optimized influence factor is given by the stepwise regression method, and the multi-source heterogeneous data fusion sequence is obtained. Finally, the global navigation satellite system(GNSS)observation data, crack displacement meter data and meteorological data obtained by Dangchuan landslide monitoring equipment in Heifangtai of Gansu are used for experimental verification. The results show that the feature selection method based on weighted correlation degree is superior to the traditional Pearson correlation coefficient method in the screening performance of landslide deformation influencing factors; compared with the traditional BP neural network, the prediction accuracy of the multi-source and multi-point heterogeneous data fusion model based on feature selection and stepwise regression is improved; the root mean square error(RMSE)is reduced by 51.8%, the mean absolute percentage error(MAPE)is reduced by 2.26%, and the goodness of fit reaches 0.964.

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Last Update: 2023-05-30