Bayesian Methods for Landslide Risk Assessment

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dc.contributor.advisorMedina-Cetina, Zenon
dc.contributor.committeeMemberAubeny, Charles
dc.contributor.committeeMemberLoisel, Julie
dc.contributor.committeeMemberCochran, Matthew
dc.creatorAlvarado Franco, Juan Pablo
dc.date.accessioned2024-07-30T23:03:07Z
dc.date.created2023-12
dc.date.issued2023-12-05
dc.date.submittedDecember 2023
dc.date.updated2024-07-30T23:03:08Z
dc.description.abstractLandslide hazard and risk assessment is crucial in understanding and mitigating the adverse impacts of such natural disasters. While essential, traditional assessment approaches often grapple with integrating diverse data and managing inherent uncertainties. This research addresses this gap by exploring the applicability of Bayesian Methods for landslide risk assessment. This study is presented through three distinct yet interconnected applications. The first application delves into the Bayesian probabilistic calibration in the Oregon Coastal Range, emphasizing the importance of observational data in refining parameter estimation and predictions. The second application extends the Bayesian calibration methodology to produce submarine landslide hazard maps, updating probability distributions using observations of undrained shear strength. Finally, the research introduces a novel combination of Bayesian Networks and Geographic Information Systems (BN+GIS) to assess the risk to onshore pipelines from landslides. Through these applications, the research demonstrates the robustness and adaptability of Bayesian methods and presents a sequential and systematic approach to landslide risk assessment. As landslides become an increasing concern due to climatic changes, the Bayesian methodologies presented in this dissertation offer a deeper understanding and practical tools for decision-makers in the context of risk assessment.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/1969.1/203076
dc.language.isoen
dc.subjectBayesian Methods
dc.subjectLandslides
dc.subjectLandslide Risk Assessment
dc.subjectBayesian Paradigm
dc.subjectBayesian Networks
dc.titleBayesian Methods for Landslide Risk Assessment
dc.typeThesis
dc.type.materialtext
local.embargo.lift2025-12-01
local.embargo.terms2025-12-01
local.etdauthor.orcid0009-0003-1535-0548
thesis.degree.departmentCivil and Environmental Engineering
thesis.degree.disciplineCivil Engineering
thesis.degree.grantorTexas A&M University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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