A stochastic rainfall model for reliability analysis of rainfall-induced landslides.
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| Title: | A stochastic rainfall model for reliability analysis of rainfall-induced landslides. |
|---|---|
| Authors: | Lu, Meng1 (AUTHOR), Wang, Huaan2 (AUTHOR), Sharma, Atma1 (AUTHOR), Zhang, Jie1 (AUTHOR) cezhangjie@tongji.edu.cn |
| Source: | Georisk: Assessment & Management of Risk for Engineered Systems & Geohazards. Mar2025, Vol. 19 Issue 1, p30-44. 15p. |
| Subjects: | Rainfall reliability, Landslide hazard analysis, Rainfall, Poisson processes, Model theory, Landslides |
| Abstract: | Rainfall is an important trigger for a large number of landslides. Physics-based quantitative risk assessment of rainfall-induced landslides requires modelling of rainfall uncertainty in addition to soil parameters. Explicit modelling of rainfall uncertainty should consider the uncertainties in rainfall intensity, duration, as well as the occurrence of rainfall events. Towards this aspect, this study proposes a stochastic rainfall model, where the joint distribution of rainfall duration and intensity is constructed using copula theory and the occurrence of rainfall events is described using Poisson process. Based on the proposed stochastic rainfall model, a computationally efficient reliability method with a machine learning-based surrogate model is developed for assessing the failure probability of a slope subjected to rainfall infiltration within a given time period. An illustrative example with real rainfall data from Singapore is utilised to demonstrate the proposed approach. The results suggest that the slope failure probability is less sensitive to the uncertainty in rainfall occurrence, but highly sensitive to the adopted inter-event time definition for characterisation of the rainfall event. Overall, this study provides a useful stochastic rainfall model and some practical guidelines for quantitative risk assessment of rainfall-induced landslides. [ABSTRACT FROM AUTHOR] |
| Copyright of Georisk: Assessment & Management of Risk for Engineered Systems & Geohazards is the property of Taylor & Francis Ltd 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 182980872 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A stochastic rainfall model for reliability analysis of rainfall-induced landslides. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lu%2C+Meng%22">Lu, Meng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Huaan%22">Wang, Huaan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sharma%2C+Atma%22">Sharma, Atma</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Jie%22">Zhang, Jie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> cezhangjie@tongji.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Georisk%3A+Assessment+%26+Management+of+Risk+for+Engineered+Systems+%26+Geohazards%22">Georisk: Assessment & Management of Risk for Engineered Systems & Geohazards</searchLink>. Mar2025, Vol. 19 Issue 1, p30-44. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Rainfall+reliability%22">Rainfall reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Landslide+hazard+analysis%22">Landslide hazard analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Rainfall%22">Rainfall</searchLink><br /><searchLink fieldCode="DE" term="%22Poisson+processes%22">Poisson processes</searchLink><br /><searchLink fieldCode="DE" term="%22Model+theory%22">Model theory</searchLink><br /><searchLink fieldCode="DE" term="%22Landslides%22">Landslides</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Rainfall is an important trigger for a large number of landslides. Physics-based quantitative risk assessment of rainfall-induced landslides requires modelling of rainfall uncertainty in addition to soil parameters. Explicit modelling of rainfall uncertainty should consider the uncertainties in rainfall intensity, duration, as well as the occurrence of rainfall events. Towards this aspect, this study proposes a stochastic rainfall model, where the joint distribution of rainfall duration and intensity is constructed using copula theory and the occurrence of rainfall events is described using Poisson process. Based on the proposed stochastic rainfall model, a computationally efficient reliability method with a machine learning-based surrogate model is developed for assessing the failure probability of a slope subjected to rainfall infiltration within a given time period. An illustrative example with real rainfall data from Singapore is utilised to demonstrate the proposed approach. The results suggest that the slope failure probability is less sensitive to the uncertainty in rainfall occurrence, but highly sensitive to the adopted inter-event time definition for characterisation of the rainfall event. Overall, this study provides a useful stochastic rainfall model and some practical guidelines for quantitative risk assessment of rainfall-induced landslides. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Georisk: Assessment & Management of Risk for Engineered Systems & Geohazards is the property of Taylor & Francis Ltd 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/17499518.2024.2359957 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 30 Subjects: – SubjectFull: Rainfall reliability Type: general – SubjectFull: Landslide hazard analysis Type: general – SubjectFull: Rainfall Type: general – SubjectFull: Poisson processes Type: general – SubjectFull: Model theory Type: general – SubjectFull: Landslides Type: general Titles: – TitleFull: A stochastic rainfall model for reliability analysis of rainfall-induced landslides. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lu, Meng – PersonEntity: Name: NameFull: Wang, Huaan – PersonEntity: Name: NameFull: Sharma, Atma – PersonEntity: Name: NameFull: Zhang, Jie IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 17499518 Numbering: – Type: volume Value: 19 – Type: issue Value: 1 Titles: – TitleFull: Georisk: Assessment & Management of Risk for Engineered Systems & Geohazards Type: main |
| ResultId | 1 |
