Sparse Gaussian Process Regression for Landslide Displacement Time-Series Forecasting

Yang, Weiqi and Feng, Yuran and Wan, Jian and Wang, Lingling (2022) Sparse Gaussian Process Regression for Landslide Displacement Time-Series Forecasting. Frontiers in Earth Science, 10. ISSN 2296-6463

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Abstract

Landslide hazards are complex nonlinear systems with a highly dynamic nature. Accurate forecasting of landslide displacement and evolution is crucial for the prevention and mitigation of landslide hazards. In this study, a probabilistic landslide displacement forecasting model based on the quantification of epistemic uncertainty is proposed. In particular, the displacement forecasting problem is cast as a time-series regression problem with limited training samples and must be solved by statistical inference. The epistemic uncertainty of the landslide displacement series is depicted by the statistical properties of the function space constituted by the nonlinear mappings generated by the sparse Gaussian process regression. Data for our study was collected from the study area located in northwestern China. Other state-of-the-art probabilistic forecasting models have also been utilized for comparative analysis. The experimental results confirmed the superiority of the sparse Gaussian process in the modeling of landslide displacement series in terms of forecasting accuracy, uncertainty quantification, and robustness to overfitting.

Item Type: Article
Subjects: Digital Open Archives > Geological Science
Depositing User: Unnamed user with email support@digiopenarchives.com
Date Deposited: 13 Mar 2023 09:47
Last Modified: 01 Aug 2024 08:47
URI: http://geographical.openuniversityarchive.com/id/eprint/610

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