Prediction of landslide displacement using multi-kernel extreme learning machine and maximum information coefficient based on variational mode decomposition: a case study in Shaanxi, China

Qing Ling, Qin Zhang (), Jing Zhang, Lingjie Kong, Weiqi Zhang and Li Zhu
Additional contact information
Qing Ling: Lanzhou University of Technology
Qin Zhang: Chang’ an University
Jing Zhang: Chang’ an University
Lingjie Kong: Lanzhou University of Technology
Weiqi Zhang: The First Geodetic Team of the Ministry of Natural Resources
Li Zhu: Information Engineering University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 108, issue 1, No 40, 925-946

Abstract: Abstract Prediction of landslide movement is an efficient approach in the reduction in landslide risk. However, it is also a tough task due to the scientific challenges in forecasting a sophisticated natural disaster. This paper proposes a VMD-MIC-M-KELM (variational mode decomposition-maximum information coefficient-multi-kernel extreme learning machine) technique for prediction of landslide movements. The original displacement is first decomposed into a predefined number of components by VMD. Then, the triggers of each component are selected based on MIC between subseries and influencing factors. The decomposed terms are predicted by M-KELM respectively via k-fold cross-validation. Finally, predicted total displacement is achieved by summing up all forecasting subseries. A case study of Miaodian landslide (China) is presented for validation of the developed model. The verification results demonstrate the higher ability of the approach to forecast monthly displacement for periods up to 12 months as compared to the Poly-KELM and SVR models. Thus, improved monthly predictions may be achieved with constantly updated datasets from the monitoring system, which would offer reliable information for early warning of landslide.

Keywords: Kernel extreme learning machine; Kernel functions; Variational mode decomposition; Maximum information coefficient; Displacement prediction (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://link.springer.com/10.1007/s11069-021-04713-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:108:y:2021:i:1:d:10.1007_s11069-021-04713-w

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-021-04713-w

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().