Zobrazeno 1 - 10
of 219
pro vyhledávání: '"landslide 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:
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:
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
Publikováno v:
Remote Sensing, Vol 16, Iss 8, p 1344 (2024)
This article offers a comprehensive AI-centric review of deep learning in exploring landslides with remote-sensing techniques, breaking new ground beyond traditional methodologies. We categorize deep learning tasks into five key frameworks—classifi
Externí odkaz:
https://doaj.org/article/9df02793f6174279aa5adaacbbc95e65
Autor:
Zhao Wang, Jiakui Tang, Shengshan Hou, Yanjiao Wang, Anan Zhang, Jiru Wang, Wuhua Wang, Zhen Feng, Ang Li, Bing Han
Publikováno v:
Frontiers in Environmental Science, Vol 11 (2023)
Time series Autoregressive Integrated Moving Average (ARIMA) model is often used in landslide prediction and forecasting. However, few conditions have been suggested for the application of ARIMA models in landslide displacement prediction. This paper
Externí odkaz:
https://doaj.org/article/e416c638e68b42d5bb3402a2bd910bee
Autor:
Xuebin Xie, Yingling Huang
Publikováno v:
Mathematics, Vol 12, Iss 7, p 1001 (2024)
Landslide displacement prediction is of great significance for the prevention and early warning of slope hazards. In order to enhance the extraction of landslide historical monitoring signals, a landslide displacement prediction method is proposed ba
Externí odkaz:
https://doaj.org/article/ec1ee584dc4d4dfe8d256b85ead93766
Publikováno v:
Remote Sensing, Vol 16, Iss 4, p 618 (2024)
Rainfall and reservoir water level are commonly regarded as the two major influencing factors for reservoir landslides and are employed for landslide displacement prediction, yet their daily data are readily available with current monitoring technolo
Externí odkaz:
https://doaj.org/article/c2748a6529074743a2418c33e0cbafc7
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