Next Article in Journal / Special Issue
Kinematic Precise Point Positioning Using Multi-Constellation Global Navigation Satellite System (GNSS) Observations
Previous Article in Journal / Special Issue
Crustal and Upper Mantle Density Structure Beneath the Qinghai-Tibet Plateau and Surrounding Areas Derived from EGM2008 Geoid Anomalies
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2017, 6(1), 5; doi:10.3390/ijgi6010005

Combined Forecasting Method of Landslide Deformation Based on MEEMD, Approximate Entropy, and WLS-SVM

1
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
2
Research Center of Precise Engineering Surveying, Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541004, China
*
Authors to whom correspondence should be addressed.
Received: 12 October 2016 / Accepted: 19 December 2016 / Published: 1 January 2017
(This article belongs to the Special Issue Recent Advances in Geodesy & Its Applications)
View Full-Text   |   Download PDF [2593 KB, uploaded 6 January 2017]   |  

Abstract

Given the chaotic characteristics of the time series of landslides, a new method based on modified ensemble empirical mode decomposition (MEEMD), approximate entropy and the weighted least square support vector machine (WLS-SVM) was proposed. The method mainly started from the chaotic sequence of time-frequency analysis and improved the model performance as follows: first a deformation time series was decomposed into a series of subsequences with significantly different complexity using MEEMD. Then the approximate entropy method was used to generate a new subsequence for the combination of subsequences with similar complexity, which could effectively concentrate the component feature information and reduce the computational scale. Finally the WLS-SVM prediction model was established for each new subsequence. At the same time, phase space reconstruction theory and the grid search method were used to select the input dimension and the optimal parameters of the model, and then the superposition of each predicted value was the final forecasting result. Taking the landslide deformation data of Danba as an example, the experiments were carried out and compared with wavelet neural network, support vector machine, least square support vector machine and various combination schemes. The experimental results show that the algorithm has high prediction accuracy. It can ensure a better prediction effect even in landslide deformation periods of rapid fluctuation, and it can also better control the residual value and effectively reduce the error interval. View Full-Text
Keywords: modified ensemble empirical mode decomposition; approximate entropy; phase space reconstruction; weighted least squares support vector machine; accuracy evaluation modified ensemble empirical mode decomposition; approximate entropy; phase space reconstruction; weighted least squares support vector machine; accuracy evaluation
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Xie, S.; Liang, Y.; Zheng, Z.; Liu, H. Combined Forecasting Method of Landslide Deformation Based on MEEMD, Approximate Entropy, and WLS-SVM. ISPRS Int. J. Geo-Inf. 2017, 6, 5.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top