Simultaneous state–parameter estimation of rainfall-induced landslide displacement using data assimilation
<p>Landslide displacement prediction has great practical engineering significance to landslide stability evaluation and early warning. The evolution of landslide is a complex dynamic process, and applying a classical prediction method will result in significant error. The data assimilation...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2019-07-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | https://www.nat-hazards-earth-syst-sci.net/19/1387/2019/nhess-19-1387-2019.pdf |
| Summary: | <p>Landslide displacement prediction has great practical engineering
significance to landslide stability evaluation and early warning. The
evolution of landslide is a complex dynamic process, and applying a classical
prediction method will result in significant error. The data assimilation method
offers a new way to merge multisource data with the model. However, data
assimilation is still deficient in the ability to meet the demand of dynamic
landslide systems. In this paper, simultaneous state and parameter
estimation (SSPE) using particle-filter-based data assimilation is applied
to predict displacement of the landslide. A landslide SSPE assimilation
strategy can make use of time-series displacements and hydrological
information for the joint estimation of landslide displacement and model
parameters, which can improve the performance considerably. We select Xishan Village, Sichuan Province, China, as the experiment site to test the SSPE
assimilation strategy. Based on the comparison of actual monitoring data
with prediction values, results strongly suggest the effectiveness and
feasibility of the SSPE assimilation strategy in short-term landslide
displacement estimation.</p> |
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| ISSN: | 1561-8633 1684-9981 |