Landslide Hazard Modeling in Ventura and Santa Barbara Counties, California Using Multi-tiered Geospatial Data Analysis

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dc.contributor.advisorHagelman, Ronald R.
dc.contributor.authorLila, Reni Bibriven
dc.contributor.committeeMemberButler, David
dc.contributor.committeeMemberZhan, Benjamin
dc.contributor.committeeMemberTobin, Graham A.
dc.date.accessioned2018-08-13T17:26:46Z
dc.date.available2018-08-13T17:26:46Z
dc.date.issued2018-05
dc.description.abstractPopulation growth and sprawling urbanization have resulted in higher perturbations of susceptible landscapes and more people and infrastructure exposed to hazardous landslides in southern California. This, in turn, has resulted in an increase in both frequency and magnitude of landslide disasters in the region. Landslides impact thousands of people and damage billions of dollars of infrastructure each year. Mitigation and response to these disasters can be difficult and expensive especially when reliable, high-resolution risk and hazard exposure maps are rarely available to local planners and managers at scales that can be efficiently utilized for local decision-making. Several methods for assessing landslide hazards have been proposed and implemented over the years. However, a portable, high-resolution method of assessing and visualizing landslide risk and hazard exposure remains elusive. This research provides a two-step method, enabled by geographic information systems (GIS) and multi-criteria quantitative analysis, to produce a high-resolution spatial analysis of both geophysical landslide risk and landslide hazard exposure for the built environment. Phase I of this study develops and deploys a GIS-based method for landslide risk assessment using selected geophysical attributes, including past landslide and wildfire experience, to model landslide risk within the study area of Ventura County and Santa Barbara County, California. Phase II leverages the high-resolution quantitative risk results from Phase I to develop a landslide hazard exposure model that illustrates the likelihood of landslides interacting with features of the built environment within the study area. The resulting hazard exposure model provides a reliable, efficient ranking of potential landslide hazard exposure for each building parcel within the study area based on the integrated geophysical risk model, the geomorphological attributes of the study area and the spatial density of the built environment. This research demonstrates that, by leveraging a multi-tiered modeling process that involved both primary and secondary data, Geoscientists and hazards managers can develop high-resolution landslide risk and hazard assessments suitable for land-use and settlement planning at the local scale. In applying this approach, hazard exposure mapping can play a renewed role in assessing areas with high landslide hazards and helping mitigate the associated risks.
dc.description.departmentGeography and Environmental Studies
dc.formatText
dc.format.extent146 pages
dc.format.medium1 file (.pdf)
dc.identifier.citationLila, R. B. (2018). <i>Landslide hazard modeling in Ventura and Santa Barbara counties, California using multi-tiered geospatial data analysis</i> (Unpublished dissertation). Texas State University, San Marcos, Texas.
dc.identifier.urihttps://hdl.handle.net/10877/7481
dc.language.isoen
dc.subjectLandslides
dc.subjectMulti-criteria
dc.subjectHazard
dc.subjectExposure
dc.subjectProximity
dc.subjectSusceptibility
dc.subjectProbability
dc.subjectFuzzy membership
dc.subjectVulnerability
dc.subjectQuantification
dc.subject.lcshLandslides--California--Santa Barbara Countyen_US
dc.subject.lcshLandslides--California--Ventura Countyen_US
dc.subject.lcshGeographic information systemsen_US
dc.subject.lcshHazard mitigationen_US
dc.titleLandslide Hazard Modeling in Ventura and Santa Barbara Counties, California Using Multi-tiered Geospatial Data Analysis
dc.typeDissertation
thesis.degree.departmentGeography
thesis.degree.disciplineEnvironmental Geography
thesis.degree.grantorTexas State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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