Risk Analysis Commons^™

Modeling Disaster Resilience Through A Human-Centered Lens: Exposure, Vulnerability And Adaptation, Tong Liu

All Dissertations

The core of reframing and operationalizing disaster resilience with a human-centered lens is to incorporate concepts from socio-ecological resilience into engineering resilience to better understand the humans’ capability for disaster adaptation. Existing studies have drawn practical implications by identifying actionable thresholds for infrastructure systems under disasters, which can be easily applied by policymakers, emergency managers and municipal agencies. However, how individuals interact with, respond to, or adapt under these infrastructure thresholds remain understudied. This hinders the operationalization of disaster resilience at the human scale.

First, I examined exposure by analyzing how configuration and distribution of urban infrastructure systems, such as …

Toward The Application Of Natural Language Processing In Electronic Health Record Analysis For Taxonomy Development, Latoya Mcdonald

All Dissertations

Electronic health records (EHRs) are pivotal resources for nurse practice because they increase the timeliness and reliability of patient information at the point of care and support access by multiple healthcare providers and the individual patients themselves. However, it is widely recognized that data extraction from EHRs is challenging due to the variability in the language used in clinical care notes and the lack of standardized terminology across healthcare systems. The broad objective of this dissertation is to develop taxonomy-based classification models for nursing care by applying feature engineering approaches to EHRs that include nursing care of ostomy patients following …

Quantifying Hurricane Effects On Housing: Evaluating Damage, Loss, And Shelter Demands Using Historical And Simulated Storm Tracks, Adish Deep Shakya

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This research introduces an advanced framework which employs parametric wind field models for peak wind speeds, and building fragility curves, loss functions, and demographic data to estimate for estimating housing damage and loss. The uninhabitable units immediate displaced households, short-term and long-term shelter need households are determined. with a particular focus on those eligible for FEMA assistance. The framework's validity is reinforced by a high correlation in the analysis of recent hurricane events between estimated numbers of displaced households and actual FEMA aid recipients, where FEMA aids about 20-60% of the predicted long-term displaced households. A novel application of the …

Addressing Mercury Contamination From Artisanal Gold Mining In Colombia: Pollution Exposure, Health Risk, And Educational Efforts In The Communities Of La Toma, Colombia, Kelli Mccourt

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Artisanal and small-scale gold mining (ASGM) has become a primary environmental and public health concern in the region of Colombia known as Alto Cauca. The predominantly Afro-descendent communities of Yolombó and La Toma in Alto Cauca have experienced pollution linked to the use of mercury in the ASGM process. For the past decade, mercury has emerged as a contaminant of increasing concern in the communities due to its toxicity and ability to bioaccumulate and biomagnify. Likewise, mining in the region raises concerns over the leaching of metals into local waters. Given the complexity of risk and pollution in the communities, …

Integrating Sensor Development, Risk Assessment, And Community Engagement To Support Environmental Justice In The Rural Community Of El Tiple, Colombia, David Bahamon Pinzon

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In Colombia, ethnic communities have traditionally been responsible stewards of natural resources. They recognize the importance of these resources for their livelihood, as well as their ancestral and cultural heritage. El Tiple, a rural Afro-Colombian community, has been affected by the incursion of private corporations that promoted the expansion of sugarcane monocrops in their territory. Since the introduction of the monoculture industry, local freshwater sources have been depleted due to intensive water use for irrigation of the sugarcane crops. Additionally, the intensive usage of agrochemicals has been linked with loss of native flora, damages to family farms, and pollution of …

Machine Learning-Based Data And Model Driven Bayesian Uncertanity Quantification Of Inverse Problems For Suspended Non-Structural System, Zhiyuan Qin

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Inverse problems involve extracting the internal structure of a physical system from noisy measurement data. In many fields, the Bayesian inference is used to address the ill-conditioned nature of the inverse problem by incorporating prior information through an initial distribution. In the nonparametric Bayesian framework, surrogate models such as Gaussian Processes or Deep Neural Networks are used as flexible and effective probabilistic modeling tools to overcome the high-dimensional curse and reduce computational costs. In practical systems and computer models, uncertainties can be addressed through parameter calibration, sensitivity analysis, and uncertainty quantification, leading to improved reliability and robustness of decision and …

Wetland Uranium Transport Via Iron-Organic Matter Flocs And Hyporheic Exchange, Connor J. Parker

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Uranium (U) released from the M-Area at the Department of Energy Savannah River Site into Tims Branch, a seasonal wetland and braided stream system, is estimated to be 43,500 kg between 1965 and 1984. The motivation for this work is the uranium’s persistence in the wetland for decades, where it is estimated that 80% of the U currently remains in the Tims Branch wetland. U has begun to incorporate into wetland iron (Fe) and carbon cycles, associating with local Fe mineralogy and deposits of rich wetland organic matter (OM). The objective of this work is to characterize the chemical phases …

Application Of Image Processing And Convolutional Neural Networks For Flood Image Classification And Semantic Segmentation, Jaku Rabinder Rakshit Pally

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Floods are among the most destructive natural hazards that affect millions of people across the world leading to severe loss of life and damage to property, critical infrastructure, and the environment. Deep learning algorithms are exceptionally valuable tools for collecting and analyzing the catastrophic readiness and countless actionable flood data. Convolutional neural networks (CNNs) are one form of deep learning algorithms widely used in computer vision which can be used to study flood images and assign learnable weights and biases to various objects in the image. Here, we leveraged and discussed how connected vision systems can be used to embed …

Seismic Performance Assessment Of Building Contents: Monetary Losses And Injuries, Sereen Majdalaweyh

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Building contents include all the components that are not attached to the building which the owners place after the construction phase, such as furniture, electrical equipment, glassware, and other personal items. Loss and damage assessment of building contents proved to be challenging in performance-based earthquake engineering frameworks because of the data sparsity. Damages to building contents during an earthquake not only cause monetary losses; tumbling and over-toppling of heavy building contents could result in injuries and even deaths of occupants. While major advancements have been made in performance-based earthquake engineering; however, the focus is mainly on damages and collapse risk …