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Case study of a landslide continuous probability rainfall threshold analysis based on the prediction interval principle

Bazhong City is located on stratum dominated by red-bed conditions. This type of weak geological condition with sand and mudstone interbedding is very easily affected by climatic conditions to produce rainfall-type landslides. On the basis of landslide data statistics collected in Bazhong City from...

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Autores principales: Huang, Yu, Zhao, Cuizhu, Jin, Xiaoyan, Zhu, Yan, Peng, Ming, Chen, Zhiyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9918516/
https://www.ncbi.nlm.nih.gov/pubmed/36765198
http://dx.doi.org/10.1038/s41598-023-29625-6
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author Huang, Yu
Zhao, Cuizhu
Jin, Xiaoyan
Zhu, Yan
Peng, Ming
Chen, Zhiyi
author_facet Huang, Yu
Zhao, Cuizhu
Jin, Xiaoyan
Zhu, Yan
Peng, Ming
Chen, Zhiyi
author_sort Huang, Yu
collection PubMed
description Bazhong City is located on stratum dominated by red-bed conditions. This type of weak geological condition with sand and mudstone interbedding is very easily affected by climatic conditions to produce rainfall-type landslides. On the basis of landslide data statistics collected in Bazhong City from 2011 to 2019, this paper uses ERA5-Land rainfall data to statistically analyze the correlation between rainfall and landslide events in Bazhong City. The landslide events in Bazhong City are greatly affected by rainfall events lasting for 10 days. Considering the influence of rainfall seepage and other processes, an effective cumulative rainfall-duration threshold curve for Bazhong City is obtained via median nonlinear fitting. Then, on the basis of the prediction interval, the rainfall threshold formula under different landslide occurrence probabilities is obtained and the critical threshold curve with a non-exceeding probability of 1% in Bazhong City is calculated and verified. Subsequently, a continuous probability distribution fitting function of landslide occurrence is established and a continuous probability distribution surface with a good fitting effect in Bazhong City is obtained. This allows a definite probability of whether future rainfall events will induce landslides to be obtained, providing an important basis for engineering disaster prevention and mitigation.
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spelling pubmed-99185162023-02-12 Case study of a landslide continuous probability rainfall threshold analysis based on the prediction interval principle Huang, Yu Zhao, Cuizhu Jin, Xiaoyan Zhu, Yan Peng, Ming Chen, Zhiyi Sci Rep Article Bazhong City is located on stratum dominated by red-bed conditions. This type of weak geological condition with sand and mudstone interbedding is very easily affected by climatic conditions to produce rainfall-type landslides. On the basis of landslide data statistics collected in Bazhong City from 2011 to 2019, this paper uses ERA5-Land rainfall data to statistically analyze the correlation between rainfall and landslide events in Bazhong City. The landslide events in Bazhong City are greatly affected by rainfall events lasting for 10 days. Considering the influence of rainfall seepage and other processes, an effective cumulative rainfall-duration threshold curve for Bazhong City is obtained via median nonlinear fitting. Then, on the basis of the prediction interval, the rainfall threshold formula under different landslide occurrence probabilities is obtained and the critical threshold curve with a non-exceeding probability of 1% in Bazhong City is calculated and verified. Subsequently, a continuous probability distribution fitting function of landslide occurrence is established and a continuous probability distribution surface with a good fitting effect in Bazhong City is obtained. This allows a definite probability of whether future rainfall events will induce landslides to be obtained, providing an important basis for engineering disaster prevention and mitigation. Nature Publishing Group UK 2023-02-10 /pmc/articles/PMC9918516/ /pubmed/36765198 http://dx.doi.org/10.1038/s41598-023-29625-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Huang, Yu
Zhao, Cuizhu
Jin, Xiaoyan
Zhu, Yan
Peng, Ming
Chen, Zhiyi
Case study of a landslide continuous probability rainfall threshold analysis based on the prediction interval principle
title Case study of a landslide continuous probability rainfall threshold analysis based on the prediction interval principle
title_full Case study of a landslide continuous probability rainfall threshold analysis based on the prediction interval principle
title_fullStr Case study of a landslide continuous probability rainfall threshold analysis based on the prediction interval principle
title_full_unstemmed Case study of a landslide continuous probability rainfall threshold analysis based on the prediction interval principle
title_short Case study of a landslide continuous probability rainfall threshold analysis based on the prediction interval principle
title_sort case study of a landslide continuous probability rainfall threshold analysis based on the prediction interval principle
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9918516/
https://www.ncbi.nlm.nih.gov/pubmed/36765198
http://dx.doi.org/10.1038/s41598-023-29625-6
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