A dynamic prediction model of landslide displacement based on VMD–SSO–LSTM approach

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: 1 Online-Ressource.

Language: Englisch

Bibliographic citation: A dynamic prediction model of landslide displacement based on VMD–SSO–LSTM approach ; volume:14 ; number:1 ; day:22 ; month:4 ; year:2024 ; pages:1-14 ; date:12.2024
Scientific reports ; 14, Heft 1 (22.4.2024), 1-14, 12.2024

Creator: Wang, Haiying
Ao, Yang
Wang, Chenguang
Zhang, Yingzhi
Zhang, Xiaofeng

Contributor: SpringerLink (Online service)

DOI: 10.1038/s41598-024-59517-2

URN: urn:nbn:de:101:1-2407082108226.126444322964

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:55 AM CEST

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Wang, Haiying
Ao, Yang
Wang, Chenguang
Zhang, Yingzhi
Zhang, Xiaofeng
SpringerLink (Online service)

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A dynamic prediction model of landslide displacement based on VMD–SSO–LSTM approach

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Other Objects (12)

Landslide displacement prediction using time series InSAR with combined LSTM and TCN: application to the Xiao Andong landslide, Yunnan Province, China

Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study

Displacement residuals reveal landslide regime shifts

A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms

Monitoring and prediction of landslide-related deformation based on the GCN-LSTM algorithm and SAR imagery

Reliability-enhanced surrogate-assisted particle swarm optimization for feature selection and hyperparameter optimization in landslide displacement prediction

Harnessing LSTM and XGBoost algorithms for storm prediction

The Prediction of COVID-19 Using LSTM Algorithms

Dense 3D displacement vector fields for point cloud-based landslide monitoring

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Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study

Displacement residuals reveal landslide regime shifts

A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms

Monitoring and prediction of landslide-related deformation based on the GCN-LSTM algorithm and SAR imagery

Reliability-enhanced surrogate-assisted particle swarm optimization for feature selection and hyperparameter optimization in landslide displacement prediction

Harnessing LSTM and XGBoost algorithms for storm prediction

The Prediction of COVID-19 Using LSTM Algorithms

Dense 3D displacement vector fields for point cloud-based landslide monitoring

Eye Tracking-based LSTM for Locomotion Prediction in VR

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Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study

Displacement residuals reveal landslide regime shifts

A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms

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Reliability-enhanced surrogate-assisted particle swarm optimization for feature selection and hyperparameter optimization in landslide displacement prediction

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