UNCERTAINTY QUANTIFICATION OF LANDSLIDE SUSCEPTIBILITY MAPPING USING BAYESIAN NETWORK

Landslides account for an average of 25 fatalities and monetary costs exceeding $2 billion USD annually, according to the U.S. Geological Survey (USGS). Landslide susceptibility mapping (LSM) can provide valuable insights for landslide characterization and helps optimize risk mitigation strategies. To enhance the reliability of LSM, uncertainty in landslides characterization must be identified and quantified. There are longstanding and systematic uncertainties in LSM that remain unaddressed or inadequately resolved in the literature, including (1) the impact of boundary geometry on landslide characterization, (2) challenges in defining negative samples (i.e., non-landslide points) for machine learning-based model training, and (3) interpreting the causal relationships among factors influencing landslides and uncertainty propagation in model predictions. To address these knowledge gaps, this work presents results of 1) sensitivity analysis to assess the impact of varying mapping geometry of the landslides on the LSM outcomes, 2) an uncertainty quantification of different negative sample scenarios on LSM development using a Bayesian network model, and 3) a comparative analysis between static and dynamic model structures incorporating a physical slope stability model within a probabilistic machine learning framework. These analyses explore uncertainty derived from mapping, sampling, and modeling perspectives, hence providing valuable insights into future robust LSMs and improved understanding of the causal relationships between geo-environmental conditions and landslide occurrences.

Grantor dc:publisher

Temple University. Libraries

Year dc:date.issued

2023

Author dc:creator

• Khabiri, Sahand

Advisor dc:contributor.advisor

• Zhu, Yichuan

Committee members dc:contributor.committeemember

• Coe, Joseph T.
• Khanzadeh, Mehdi
• Yuan, Heyang (Harry)
• Davatzes, Nicholas

• Civil engineering

Statement dc:rights

• IN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available.

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/20.500.12613/9537

OAI identifier oai:identifier

oai:scholarshare.temple.edu:20.500.12613/9537