A new hybrid method for establishing point forecasting, interval forecasting, and probabilistic forecasting of landslide displacement

Hong Wang (), Guangyu Long (), Jianxing Liao (), Yan Xu () and Yan Lv ()
Additional contact information
Hong Wang: Guizhou University
Guangyu Long: Guizhou University
Jianxing Liao: Guizhou University
Yan Xu: Jilin University
Yan Lv: Jilin University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 111, issue 2, No 16, 1479-1505

Abstract: Abstract In addition to its inherent evolution trend, landslide displacement contains strong fluctuation and randomness, and omni-directional landslide displacement prediction is more scientific than single point prediction or interval prediction. In this study, a new hybrid approach composed of double exponential smoothing (DES), variational mode decomposition (VMD), long short-term memory network (LSTM), and Gaussian process regression (GPR) is proposed for the point, interval, and probabilistic prediction of landslide displacement. The proposed model includes two parts: (i) predicting the inherent evolution trend of landslide displacement through DES-VMD-LSTM; (ii) evaluating the uncertainty in the first prediction based on the GPR model. In the first part, DES is used to predict the trend displacement, and the periodic and stochastic displacement in the residual displacement is extracted by VMD and predicted by the LSTM. The triggering factors of the periodic and stochastic displacement are screened by the maximum information coefficient (MIC), and the screened factors are decomposed into low- and high-frequency components by VMD to predict the periodic and stochastic displacements, respectively. The first cumulative displacement prediction results are achieved by adding the predicted trend and the periodic and stochastic displacements. By setting the first predicted displacement as the input and the actual displacement as the expected output, the point, interval, and probabilistic predictions of landslide displacement are achieved through the GPR model. The plausibility of the proposed model is validated with data from the Bazimen (BZM) and Baishuihe (BSH) landslides in the Three Gorges Reservoir area. This model has the potential to achieve the deterministic prediction of landslide displacement and quantify the uncertainty contained in the displacement. A comparative study shows that this method has a high performance for the point, interval, and probabilistic prediction of landslide displacement.

Keywords: Landslide displacement prediction; Double exponential smoothing; Variational mode decomposition; Long short-term memory network; Gaussian process regression (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s11069-021-05104-x 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:111:y:2022:i:2:d:10.1007_s11069-021-05104-x

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

DOI: 10.1007/s11069-021-05104-x

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 ().