期刊
FRONTIERS IN EARTH SCIENCE
卷 10, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/feart.2022.944301
关键词
landslide displacement; time-series; probabilistic forecasting; epistemic uncertainty; sparse Gaussian process
Landslide hazards are complex nonlinear systems and accurate forecasting of landslide displacement and evolution is crucial. In this study, a probabilistic landslide displacement forecasting model based on the quantification of epistemic uncertainty is proposed, depicting the uncertainty of landslide displacement series using sparse Gaussian process regression.
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.
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