Zobrazeno 1 - 10
of 731
pro vyhledávání: '"Displacement prediction"'
Publikováno v:
Journal of Rock Mechanics and Geotechnical Engineering, Vol 16, Iss 10, Pp 4017-4033 (2024)
Landslides are destructive natural disasters that cause catastrophic damage and loss of life worldwide. Accurately predicting landslide displacement enables effective early warning and risk management. However, the limited availability of on-site mea
Externí odkaz:
https://doaj.org/article/7d2a5b54706645c097c9576fbfa49450
Publikováno v:
Results in Engineering, Vol 24, Iss , Pp 103545- (2024)
This paper aims to address the issue of uncertainty and limited analysis depth in machine learning (ML)-based slope cumulative displacement (SCD) prediction, leading to inaccuracies in slope safety assessment. Firstly, the Bootstrap algorithm is used
Externí odkaz:
https://doaj.org/article/828bc016162444d5aef76cb6481c3f95
Publikováno v:
Gong-kuang zidonghua, Vol 50, Iss 6, Pp 6-15 (2024)
In order to overcome the problem that a single information source cannot accurately characterize the evolution features of mining landslide disasters, based on multi-source information fusion technology, this paper summarizes the research progress of
Externí odkaz:
https://doaj.org/article/1c9759e18c8349629cfbe770de5e4d22
Akademický článek
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Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 133, Iss , Pp 104121- (2024)
Reservoir landslides in the Three Gorges Reservoir, China, exhibit prolonged slow motion and the potential for catastrophic events due to fluctuations in reservoir levels and intense rainfall episodes. Their distinct step-like deformation characteris
Externí odkaz:
https://doaj.org/article/3492f3f75ceb462d8a1e77537315be19
Publikováno v:
Zhongguo dizhi zaihai yu fangzhi xuebao, Vol 35, Iss 1, Pp 82-91 (2024)
Landslide displacement prediction is an important basis of predicting landslide disasters. Most of the previous landslide displacement prediction models include time series prediction models, BP neural network prediction models, Gaussian fitting pred
Externí odkaz:
https://doaj.org/article/82a69e87549a471a9780f9f6b54210c7
Publikováno v:
Dizhi lixue xuebao, Vol 30, Iss 4, Pp 633-646 (2024)
Objective Landslide-displacement prediction is critical when evaluating landslide stability. Despite the achievements of time-series methods based on deep-learning paradigms in predicting landslide displacement, the nonstationary, periodic, and trend
Externí odkaz:
https://doaj.org/article/31958aea63ea45e196f72c89f8ea0e2b
Publikováno v:
Land, Vol 13, Iss 10, p 1724 (2024)
Displacement deformation prediction is critical for landslide disaster monitoring, as a good landslide displacement prediction system helps reduce property losses and casualties. Landslides in the Three Gorges Reservoir Area (TGRA) are affected by pr
Externí odkaz:
https://doaj.org/article/d1c8bc32d59e4316ba7eaf0ed0586327
Publikováno v:
Applied Sciences, Vol 14, Iss 20, p 9288 (2024)
Landslide displacement monitoring can directly reflect the deformation process of a landslide. Predicting landslide displacements using monitored time series data through deep learning is a useful method for landslide early warning. Currently, existi
Externí odkaz:
https://doaj.org/article/a73aed8403504430a193d58829ddca45
Publikováno v:
Frontiers in Physics, Vol 12 (2024)
Accurately predicting landslide displacement is essential for reducing and managing associated risks. To address the challenges of both under-decomposition and over-decomposition in landslide displacement analysis, as well as the low predictive accur
Externí odkaz:
https://doaj.org/article/8371dccb309d4c80b8d30321e03ce9bd