12,697 research outputs found
"Last-Mile" preparation for a potential disaster
Extreme natural events, like e.g. tsunamis or earthquakes, regularly lead to catastrophes with dramatic consequences. In recent years natural disasters caused hundreds of thousands of deaths, destruction of infrastructure, disruption of economic activity and loss of billions of dollars worth of property and thus revealed considerable deficits hindering their effective management: Needs for stakeholders, decision-makers as well as for persons concerned include systematic risk identification and evaluation, a way to assess countermeasures, awareness raising and decision support systems to be employed before, during and after crisis situations. The overall goal of this study focuses on interdisciplinary integration of various scientific disciplines to contribute to a tsunami early warning information system. In comparison to most studies our focus is on high-end geometric and thematic analysis to meet the requirements of small-scale, heterogeneous and complex coastal urban systems. Data, methods and results from engineering, remote sensing and social sciences are interlinked and provide comprehensive information for disaster risk assessment, management and reduction. In detail, we combine inundation modeling, urban morphology analysis, population assessment, socio-economic analysis of the population and evacuation modeling. The interdisciplinary results eventually lead to recommendations for mitigation strategies in the fields of spatial planning or coping capacity
Efficient treatment and quantification of uncertainty in probabilistic seismic hazard and risk analysis
The main goals of this thesis are the development of a computationally efficient framework for stochastic treatment of various important uncertainties in probabilistic seismic hazard and risk assessment, its application to a newly created seismic risk model of Indonesia, and the analysis and quantification of the impact of these uncertainties on the distribution of estimated seismic losses for a large number of synthetic portfolios modeled after real-world counterparts.
The treatment and quantification of uncertainty in probabilistic seismic hazard and risk analysis has already been identified as an area that could benefit from increased research attention.
Furthermore, it has become evident that the lack of research considering the development and application of suitable sampling schemes to increase the computational efficiency of the stochastic simulation represents a bottleneck for applications where model runtime is an important factor.
In this research study, the development and state of the art of probabilistic seismic hazard and risk analysis is first reviewed and opportunities for improved treatment of uncertainties are identified.
A newly developed framework for the stochastic treatment of portfolio location uncertainty as well as ground motion and damage uncertainty is presented.
The framework is then optimized with respect to computational efficiency.
Amongst other techniques, a novel variance reduction scheme for portfolio location uncertainty is developed.
Furthermore, in this thesis, some well-known variance reduction schemes such as Quasi Monte Carlo, Latin Hypercube Sampling and MISER (locally adaptive recursive stratified sampling) are applied for the first time to seismic hazard and risk assessment.
The effectiveness and applicability of all used schemes is analyzed.
Several chapters of this monograph describe the theory, implementation and some exemplary applications of the framework.
To conduct these exemplary applications, a seismic hazard model for Indonesia was developed and used for the analysis and quantification of loss uncertainty for a large collection of synthetic portfolios.
As part of this work, the new framework was integrated into a probabilistic seismic hazard and risk assessment software suite developed and used by Munich Reinsurance Group.
Furthermore, those parts of the framework that deal with location and damage uncertainties are also used by the flood and storm natural catastrophe model development groups at Munich Reinsurance for their risk models
Report on DIMACS Working Group Meeting: Mathematical Sciences Methods for the Study of Deliberate Releases of Biological Agents and their Consequences
55 pages, 1 article*Report on DIMACS Working Group Meeting: Mathematical Sciences Methods for the Study of Deliberate Releases of Biological Agents and their Consequences* (Castillo-Chavez, Carlos; Roberts, Fred S.) 55 page
On The Application Of Computational Modeling To Complex Food Systems Issues
Transdisciplinary food systems research aims to merge insights from multiple fields, often revealing confounding, complex interactions. Computational modeling offers a means to discover patterns and formulate novel solutions to such systems-level problems. The best models serve as hubs—or boundary objects—which ground and unify a collaborative, iterative, and transdisciplinary process of stakeholder engagement. This dissertation demonstrates the application of agent-based modeling, network analytics, and evolutionary computational optimization to the pressing food systems problem areas of livestock epidemiology and global food security. It is comprised of a methodological introduction, an executive summary, three journal-article formatted chapters, and an overarching discussion section.
Chapter One employs an agent-based computer model (RUSH-PNBM v.1.1) developed to study the potential impact of the trend toward increased producer specialization on resilience to catastrophic epidemics within livestock production chains. In each run, an infection is introduced and may spread according to probabilities associated with the various modes of contact between hog producer, feed mill, and slaughter plant agents. Experimental data reveal that more-specialized systems are vulnerable to outbreaks at lower spatial densities, have more abrupt percolation transitions, and are characterized by less-predictable outcomes; suggesting that reworking network structures may represent a viable means to increase biosecurity.
Chapter Two uses a calibrated, spatially-explicit version of RUSH-PNBM (v.1.2) to model the hog production chains within three U.S. states. Key metrics are calculated after each run, some of which pertain to overall network structures, while others describe each actor’s positionality within the network. A genetic programming algorithm is then employed to search for mathematical relationships between multiple individual indicators that effectively predict each node’s vulnerability. This “meta-metric” approach could be applied to aid livestock epidemiologists in the targeting of biosecurity interventions and may also be useful to study a wide range of complex network phenomena.
Chapter Three focuses on food insecurity resulting from the projected gap between global food supply and demand over the coming decades. While no single solution has been identified, scholars suggest that investments into multiple interventions may stack together to solve the problem. However, formulating an effective plan of action requires knowledge about the level of change resulting from a given investment into each wedge, the time before that effect unfolds, the expected baseline change, and the maximum possible level of change. This chapter details an evolutionary-computational algorithm to optimize investment schedules according to the twin goals of maximizing global food security and minimizing cost. Future work will involve parameterizing the model through an expert informant advisory process to develop the existing framework into a practicable food policy decision-support tool
Optimal Uncertainty Quantification
We propose a rigorous framework for Uncertainty Quantification (UQ) in which
the UQ objectives and the assumptions/information set are brought to the
forefront. This framework, which we call \emph{Optimal Uncertainty
Quantification} (OUQ), is based on the observation that, given a set of
assumptions and information about the problem, there exist optimal bounds on
uncertainties: these are obtained as values of well-defined optimization
problems corresponding to extremizing probabilities of failure, or of
deviations, subject to the constraints imposed by the scenarios compatible with
the assumptions and information. In particular, this framework does not
implicitly impose inappropriate assumptions, nor does it repudiate relevant
information. Although OUQ optimization problems are extremely large, we show
that under general conditions they have finite-dimensional reductions. As an
application, we develop \emph{Optimal Concentration Inequalities} (OCI) of
Hoeffding and McDiarmid type. Surprisingly, these results show that
uncertainties in input parameters, which propagate to output uncertainties in
the classical sensitivity analysis paradigm, may fail to do so if the transfer
functions (or probability distributions) are imperfectly known. We show how,
for hierarchical structures, this phenomenon may lead to the non-propagation of
uncertainties or information across scales. In addition, a general algorithmic
framework is developed for OUQ and is tested on the Caltech surrogate model for
hypervelocity impact and on the seismic safety assessment of truss structures,
suggesting the feasibility of the framework for important complex systems. The
introduction of this paper provides both an overview of the paper and a
self-contained mini-tutorial about basic concepts and issues of UQ.Comment: 90 pages. Accepted for publication in SIAM Review (Expository
Research Papers). See SIAM Review for higher quality figure
An intelligent system for vulnerability and remediation assessment of flooded residential buildings
Floods are natural phenomena which are a threat to human settlements. Flooding can result in costly repairs to buildings, loss of business and, in some cases, loss of life. The forecasts for climate change show a further increased risk of flooding in future years. Accordingly, the flooding of residential property has been observed as on the rise in the UK.
It is difficult to prevent floods from occurring, but the effects of flooding can be managed in an attempt to reduce risks and costs of repair. This can be achieved through ensuring a good understanding of the problem, and thereby establishing good management systems which are capable of dealing with all aspects of the flood.
The use of an intelligent system for assessment and remediation of buildings subjected to flooding damage can facilitate the management of this problem. Such a system can provide guidance for the assessment of vulnerability and the repair of flood damaged residential buildings; this could save time and money through the use of the advantages and benefits offered by knowledge base systems.
A prototype knowledge base system has been developed in this research. The system comprises three subsystems: degree of vulnerability assessment subsystem; remediation options subsystem; and foundation damage assessment subsystem. The vulnerability assessment subsystem is used to calculate the degree of vulnerability, which will then be used by the remediation options subsystem to select remediation options strategy. The vulnerability assessment subsystem can subsequently be used to calculate the degree to which the building is vulnerable to damage by flooding even if it is not flooded. Remediation options subsystem recommended two strategy options: either ordinary remediation options in the case of vulnerability being low or, alternatively, resilience remediation options in the case of vulnerability being high. The foundation damage assessment subsystem is working alone and is used to assess the damage caused by flooding to the building s foundation, and to thereby recommend a repair option based on the damage caused and foundation type.
The system has been developed based on the knowledge acquired from different sources and methods, including survey questionnaires, documents, interviews, and workshops. The system is then evaluated by experts and professionals in the industry.
The developed system makes a contribution in the management and standardisation of residential building flooded damage and repair
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