View in EDS HTML Full Text PDF Full Text

A probabilistic early warning model of rainfall-induced landslides accounting for multiple factors and correlation structures.

Saved in:
Bibliographic Details
Title: A probabilistic early warning model of rainfall-induced landslides accounting for multiple factors and correlation structures.
Authors: Li, Yousheng1 (AUTHOR), Yan, Echuan1 (AUTHOR) yeszgdzdx@163.com, Xiao, Weibo1 (AUTHOR), Hao, Yonghao1 (AUTHOR), Peduto, Dario2 (AUTHOR)
Source: Stochastic Environmental Research & Risk Assessment. May2025, Vol. 39 Issue 5, p1947-1962. 16p.
Subjects: Rainfall probabilities, Rainfall, Architectural details, Conditional probability, Probability theory, Landslides
Abstract: The frequency and intensity of extreme rainfall events have been increasing, resulting in an escalating number of landslides. The rainfall factors that induce landslides are frequently multidimensional and possess intrinsic correlations. However, the existing probability analysis models for rainfall-induced landslides rarely consider multiple factors and their correlations, resulting in an inability to accurately reflect the effect of rainfall on landslides. To address this issue, this study proposes a landslide probability analysis model that considers multiple rainfall factors and their correlations. The paper first introduces the relevant theories used for modeling. Then, it elaborates on the method and steps of building the model in detail. Finally, it illustrates and demonstrates the effectiveness of the proposed model through an example of rainfall-Induced Landslides. The results show that: (1) Different rainfall factors show different distribution characteristics. (2) The D-vine Copula model can offer a superior degree of accuracy in the description of the structural characteristics of multidimensional rainfall factors. (3) This model has the capacity to ascertain the conditional probability and exceedance probability of landslides occurring under various conditions of rainfall. (4) The probability of landslide occurrence is significantly underestimated when the correlation between multiple rainfall factors is not taken into consideration. In summary, this method enhances the precision of probability analysis of rainfall-induced landslides. [ABSTRACT FROM AUTHOR]
Copyright of Stochastic Environmental Research & Risk Assessment is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 185782004
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A probabilistic early warning model of rainfall-induced landslides accounting for multiple factors and correlation structures.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Li%2C+Yousheng%22">Li, Yousheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yan%2C+Echuan%22">Yan, Echuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yeszgdzdx@163.com</i><br /><searchLink fieldCode="AR" term="%22Xiao%2C+Weibo%22">Xiao, Weibo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hao%2C+Yonghao%22">Hao, Yonghao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Peduto%2C+Dario%22">Peduto, Dario</searchLink><relatesTo>2</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Stochastic+Environmental+Research+%26+Risk+Assessment%22">Stochastic Environmental Research & Risk Assessment</searchLink>. May2025, Vol. 39 Issue 5, p1947-1962. 16p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Rainfall+probabilities%22">Rainfall probabilities</searchLink><br /><searchLink fieldCode="DE" term="%22Rainfall%22">Rainfall</searchLink><br /><searchLink fieldCode="DE" term="%22Architectural+details%22">Architectural details</searchLink><br /><searchLink fieldCode="DE" term="%22Conditional+probability%22">Conditional probability</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink><br /><searchLink fieldCode="DE" term="%22Landslides%22">Landslides</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The frequency and intensity of extreme rainfall events have been increasing, resulting in an escalating number of landslides. The rainfall factors that induce landslides are frequently multidimensional and possess intrinsic correlations. However, the existing probability analysis models for rainfall-induced landslides rarely consider multiple factors and their correlations, resulting in an inability to accurately reflect the effect of rainfall on landslides. To address this issue, this study proposes a landslide probability analysis model that considers multiple rainfall factors and their correlations. The paper first introduces the relevant theories used for modeling. Then, it elaborates on the method and steps of building the model in detail. Finally, it illustrates and demonstrates the effectiveness of the proposed model through an example of rainfall-Induced Landslides. The results show that: (1) Different rainfall factors show different distribution characteristics. (2) The D-vine Copula model can offer a superior degree of accuracy in the description of the structural characteristics of multidimensional rainfall factors. (3) This model has the capacity to ascertain the conditional probability and exceedance probability of landslides occurring under various conditions of rainfall. (4) The probability of landslide occurrence is significantly underestimated when the correlation between multiple rainfall factors is not taken into consideration. In summary, this method enhances the precision of probability analysis of rainfall-induced landslides. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Stochastic Environmental Research & Risk Assessment is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=185782004
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s00477-025-02950-0
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
        StartPage: 1947
    Subjects:
      – SubjectFull: Rainfall probabilities
        Type: general
      – SubjectFull: Rainfall
        Type: general
      – SubjectFull: Architectural details
        Type: general
      – SubjectFull: Conditional probability
        Type: general
      – SubjectFull: Probability theory
        Type: general
      – SubjectFull: Landslides
        Type: general
    Titles:
      – TitleFull: A probabilistic early warning model of rainfall-induced landslides accounting for multiple factors and correlation structures.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Li, Yousheng
      – PersonEntity:
          Name:
            NameFull: Yan, Echuan
      – PersonEntity:
          Name:
            NameFull: Xiao, Weibo
      – PersonEntity:
          Name:
            NameFull: Hao, Yonghao
      – PersonEntity:
          Name:
            NameFull: Peduto, Dario
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Text: May2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 14363240
          Numbering:
            – Type: volume
              Value: 39
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: Stochastic Environmental Research & Risk Assessment
              Type: main
ResultId 1