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dc.contributor.advisorRebennack, Steffen
dc.contributor.authorKrasko, Vitaliy
dc.date.accessioned2016-09-26T16:11:02Z
dc.date.accessioned2022-02-03T12:57:07Z
dc.date.available2016-09-26T16:11:02Z
dc.date.available2022-02-03T12:57:07Z
dc.date.issued2016
dc.identifierT 8134
dc.identifier.urihttps://hdl.handle.net/11124/170431
dc.descriptionIncludes bibliographical references.
dc.description2016 Fall.
dc.description.abstractDebris flows are significant natural hazards capable of causing tens of thousands of deaths and billions in damages. Post-wildfire debris flows in particular have been observed to produce more debris and can be triggered by less rainfall than typically required. Unlike earthquakes, volcanic eruptions and other large scale disasters, the probability of debris flow initiation can effectively be reduced through mitigation in addition to or instead of reducing the magnitude of the disaster conditional on occurrence. We describe a new mitigation framework and a mixed integer nonlinear programming model for mitigating a post-fire debris flow hazard. We introduce a novel two-stage multi-period decision-dependent stochastic programming model that combines the mitigation decisions with stochastic storm scenarios and emergency evacuation of injured people through routing recourse decisions. The models are applied to a case study of Santa Barbara following the 2009 Jesusita wildfire. Solutions suggest mitigation budgets should be concentrated in just two or three drainage basins in this case. Solutions to the stochastic model utilized a combination of mitigation and emergency evacuation through vehicle routing. We propose a novel mixed integer linear program for fitting continuous piecewise linear functions to discrete data, which is used to create damage functions for the debris flow hazard mitigation models. We identify the state-of-the-art globally optimal model from literature and extend it to be valid for all cases. The novel MIP is solved with a tailored solution algorithm which displays some computational advantage over the model from literature. We examine the response of human agents to natural hazard prevention and mitigation by introducing endogenous natural hazard risk to a spatial urban real estate model of the wildland urban interface. We analyze changes to structural density, welfare, housing prices and population in response to decreasing the magnitude of natural hazard risk versus decreasing the spatial extent of the hazardous area. Results suggest that decreasing the magnitude of disaster could lead to increased structural density and in the hazardous zone and potentially increase expected damages. Decreasing the spatial extent of disaster could decrease damages while increasing rents and decreasing housing size.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado School of Mines. Arthur Lakes Library
dc.relation.ispartof2016 - Mines Theses & Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.subjectdebris flows
dc.subjectglobal optimization
dc.subjectcontinuous piecewise linear fitting
dc.subjectstochastic optimization
dc.subjectdisaster operations management
dc.titleOptimal natural hazard management for post-wildfire debris flows
dc.typeText
dc.contributor.committeememberSanti, Paul M. (Paul Michael), 1964-
dc.contributor.committeememberCarbone, Jared C.
dc.contributor.committeememberMohagheghi, Salman
thesis.degree.nameDoctor of Philosophy (Ph.D.)
thesis.degree.levelDoctoral
thesis.degree.disciplineEconomics and Business
thesis.degree.grantorColorado School of Mines


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