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Effects of sampling frequency on short-term prediction of landslide displacement: A case study of Kamenziwan landslide

Published: 04 January 2021 Publication History

Abstract

Short-term prediction of surface displacement of landslide is important to short-term warning and prediction of landslide, in which the sampling frequency of monitoring data plays an important role. Taking the crack gauge monitoring data of Kamenziwan landslide in Yichang city, Hubei province as a case, the influence of the monitoring data of 1 hour, 6 hours, 12 hours and 24 hours sampling requency was studied on the prediction of landslide displacement in the next three days using XGBoost. It is found that the accuracy of the prediction model based on the monitoring data of different sampling frequencies is different and related to the time length of the historical monitoring data input by the model. These steps, which should require generation of the final output from the styled paper, are mentioned here in this paragraph. First, users have to run "Reference Numbering" from the "Reference Elements" menu; this is the first step to start the bibliography marking (it should be clicked while keeping the cursor at the beginning of the reference list). After the marking is complete, the reference element runs all the options under the "Cross Linking" menu.

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      cover image ACM Other conferences
      ISBDAI '20: Proceedings of the 2020 2nd International Conference on Big Data and Artificial Intelligence
      April 2020
      640 pages
      ISBN:9781450376457
      DOI:10.1145/3436286
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      New York, NY, United States

      Publication History

      Published: 04 January 2021

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      Author Tags

      1. Deformation analysis
      2. Displacement prediction
      3. Landslide monitoring
      4. Machine learning
      5. Sampling frequency

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      • Refereed limited

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      • Research and development of object-oriented photogrammetry by UAV(Unmanned Aerial Vehicle) and rapid deployment monitoring and early warning equipment on high-steep slope
      • Research on the technology of acquiring and integrating multi-source data of sudden geological disaster
      • Research and development and demonstration of big data monitoring and warning platform for landslide and collapse

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      ISBDAI '20

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      Overall Acceptance Rate 70 of 340 submissions, 21%

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