Disaster management and emergency response mechanisms are coming of age post 9/11. Paper based triaging and evacuation is slowly being replaced with much advanced mechanisms using remote clients (Laptops, Thin clients, PDAs), RFiDs etc. This reflects a modern trend to deploy Information Technology (IT) in disaster management. IT elements provide a great a deal of flexibility and seamlessness in the communication of information. The information flowing is so critical that, loss of data is not at all acceptable. Loss of data would mean loss of critical medical information portraying the disaster scenario. This would amount to a wrong picture being painted of the disaster incident. This basic idea led to the motivation of DiNAR (Diagnosis of Network and Application Resource). The aim of DiNAR was to remotely monitor all the components of the deployed system infrastructure (Remote clients, Servers) and if there is a fault in the infrastructure (Hardware, Software or Communication) DiNAR captures the fault alarm and do an event correlation to find the source of the problem. The biggest challenge that lies here is the fact that the entities we are trying to monitor are scattered around in the Internet. Traditional network management techniques always assume that the network is within administrative control and every device we monitor is easily reachable on demand. But the ad-hoc scenario of deployment of disaster management systems makes this task non trivial. DiNAR has been designed with an aim to work with any application which has its infrastructure elements scattered in the Internet space. DIORAMA (A real time disaster management system) represents a new series of applications (especially in medical field) where the deployment of network infrastructure is scattered around with Internet being the backbone connector. Another such example is the Intel® Health Guide PHS6000 [1], which is used in patient monitoring in homes. This thesis work uses DIORAMA as a case study application used to prove the concept of DiNAR.

Catastrophic disasters are different than routine disasters and managing them requires the mobilization of inter-organizational, inter-governmental, cross-sectoral and international humanitarian support. The role of the international community through International Nongovernmental Organizations (INGOs), and multi-lateral organizations such as the United Nations (UN) becomes imperative when the scale of the disaster is unprecedented and difficult for a country to manage on its own. The initial response and relief phase of managing disasters is one in which many agencies with different expertise, capacities, working mandates, resources, skills, working cultures and norms come together to coordinate and collaborate to provide timely response and relief services. Thus, the terrain of managing catastrophic disasters is complex and requires a deeper study to understand and delineate the factors shaping and facilitating collaborative response and relief efforts. This study examines the multi-level and multi-layered collaborative response networks present at the national-international level, provincial and district/local level of disaster response and interactions. In this research the nature and effectiveness of collaboration is being studied through a relevant case study of a catastrophic natural disaster, the 2010 Pakistan Floods. The phase of immediate response is explored primarily through Network Theory perspectives including supportive theoretical perspectives such as Social Capital, Resource Dependency, and Institutional Collective Action Theory perspectives that help to explain collaborative interactions in disaster response networks. This study explores and describes factors that influence (either facilitate or hinder) collaboration is disaster response networks. iv The key research questions for this study are: What factors facilitate and impede collaborative response to catastrophic disasters at the local, provincial, national and international levels? What are the differences and similarities in response systems at different levels? Additional questions address how leadership support (attributed to government and political leaders and organizations), institutional support (in the form of plans, international appeals of response, and development of relief funds to manage aid), network capacity of different organizations (programmatic and relational), nature of resource dependencies between responding agencies, and structural configurations of response systems impact the collaborative response in disasters. A case study method has been applied in this research. The 2010 Pakistan Floods response network/system is identified through content analysis of various newspapers, situation reports and after-action reports using the Social Network Analysis (SNA) method via UCINET Software 6.1. The actual response network is analyzed and compared with existing national disaster response plans to examine the effectiveness of collaborative response through centrality measures, clique analysis and visual display. This approach is supplemented with semistructured interviews of key institutional representatives that responded to the 2010 Floods. These organizations and institutions were primarily identified through the networks formulated via SNA. Findings and results from the analysis reflect that the response networks at each level of analysis differ both in structural aspects and also in functional aspects. The nature of the international-national response system is focused on mobilizing donor support and receiving and v managing aid, both in-kind and cash. Also a major role at the international and national level is to mobilize the UN cluster approach and focus on broader aims of response such as providing shelter and food to affected areas. Some of the factors identified as facilitating collaborative response were leadership of both national and international leaders, and availability of donor support and funds. At the provincial level of analysis, the Chief Minister of Punjab is playing a central and influential role and is partnering closely with the Armed Forces and local district administration. Interviews conducted of provincial level officials help to support the hypotheses concerning leadership support’s influence on collaborative response and also the role of institutional support in the form of creation of plans, and policies that help to mobilize quick funds and resources for relief. At the local level of response, networks are highly influenced by local conditions and local capacities of the district administration. Thus, there are diverse factors impacting each level of collaborative disaster response. All in all, leadership support, institutional support and network structural aspects are important variables that impact the effectiveness of collaborative response. Today policy makers are trying to figure out ways to collaborate successfully across sector boundaries for better and effective service delivery, both in the mundane operational tasks and in uncertain and complex situations such as disasters and catastrophic events. Thus, this research helps in expanding the literature on collaborative public management, collaborative emergency management, and network management. Also the frequency of natural disasters throughout the world demonstrate the need to study and examine factors that contribute to or hinder the effectiveness of inter-organizational response in disasters.

This research focuses on developing a cognitive radio that could operate reliably in unforeseen communications environments like those faced by the disaster and emergency response communities. Cognitive radios may also offer the potential to open up secondary or complimentary spectrum markets, effectively easing the perceived spectrum crunch while providing new competitive wireless services to the consumer. A structure and process for embedding cognition in a radio is presented, including discussion of how the mechanism was derived from the human learning process and mapped to a mathematical formalism called the BioCR. Results from the implementation and testing of the model in a hardware test bed and simulation test bench are presented, with a focus on rapidly deployable disaster communications. Research contributions include developing a biologically inspired model of cognition in a radio architecture, proposing that genetic algorithm operations could be used to realize this model, developing an algorithmic framework to realize the cognition mechanism, developing a cognitive radio simulation toolset for evaluating the behavior the cognitive engine, and using this toolset to analyze the cognitive engineà ­s performance in different operational scenarios. Specifically, this research proposes and details how the chaotic meta-knowledge search, optimization, and machine learning properties of distributed genetic algorithm operations could be used to map this model to a computable mathematical framework in conjunction with dynamic multi-stage distributed memories. The system formalism is contrasted with existing cognitive radio approaches, including traditionally brittle artificial intelligence approaches. The cognitive engine architecture and algorithmic framework is developed and introduced, including the Wireless Channel Genetic Algorithm (WCGA), Wireless System Genetic Algorithm (WSGA), and Cognitive System Monitor (CSM). Experimental results show that the cognitive engine finds the best tradeoff between a host radio's operational parameters in changing wireless conditions, while the baseline adaptive controller only increases or decreases its data rate based on a threshold, often wasting usable bandwidth or excess power when it is not needed due its inability to learn. Limitations of this approach include some situations where the engine did not respond properly due to sensitivity in algorithm parameters, exhibiting ghosting of answers, bouncing back and forth between solutions. Future research could be pursued to probe the limits of the engineà ­s operation and investigate opportunities for improvement, including how best to configure the genetic algorithms and engine mathematics to avoid engine solution errors. Future research also could include extending the cognitive engine to a cognitive radio network and investigating implications for secure communications. / Ph. D.

Yes / This paper proposes a framework to cope with the lack of data at the time of a disaster by em-ploying predictive models. The framework can be used for disaster human impact assessment based on the socio-economic characteristics of the affected countries. A panel data of 4252 natural onset disasters between 1980 to 2020 is processed through concept drift phenomenon and rule-based classifiers, namely Moving Average (MA). A Predictive model for Estimating Data (PRED) is developed as a decision-making platform based on the Disaster Severity Analysis (DSA) Technique. A comparison with the real data shows that the platform can predict the human impact of a disaster (fatality, injured, homeless) up to 3% errors; thus, it is able to inform the selection of disaster relief partners for various disaster scenarios.

Yes / ¿Part 1: The congestion of humanitarian space¿, assesses what affect the rapid proliferation of the international aid community¿s presence in Sri Lanka has had on local level relationships and emergency response capacities. It contends that the burgeoning presence of aid agencies resulted in humanitarian assistance becoming a hotly contested and competitive activity. It goes on to identify the possible factors that have contributed to the rapid congestion of this space in suggesting an explanation of why the humanitarian communities¿ normative standards appear to have failed.

Emergency managers are often faced with the toughest decisions that can ever be made, people's lives hang in the balance. Nevertheless, these tough decisions have to be made, and made quickly. There is usually too much information to process to make the best decisions. Decision support systems can relieve a significant amount of this onus, making decision while considering the complex interweaving of constraints and resources that define the boundary of the problem. We study these complex emergency management, approaching the problem with discrete optimization. Using our operational research knowledge to model mass casualty incidents, we seek to provide solutions and insights for the emergency managers. This dissertation proposes a novel deterministic model to optimize the casualty transportation and treatment decisions in response to a MCI. This deterministic model expands on current state of the art by; (1) including multiple dynamic resources that impact the various interconnected decisions, (2) further refining a survival function to measure expected survivors, (3) defining novel objective functions that consider competing priorities, including maximizing survivors and balancing equity, and finally (4) developing a MCI response simulation that provides insights to how optimization models could be used as decision-support mechanisms. / Doctor of Philosophy / Emergency managers are often faced with the toughest decisions that can ever be made, people's lives hang in the balance. Nevertheless, these tough decisions have to be made, and made quickly. But to make the best decisions, there is usually too much information to process. Computers and support tools can relieve a significant amount of this onus, making decision while considering the complex interweaving of constraints and resources that define the boundary of the problem. This dissertation provides a mathematical model that relates the important decisions made during a MCI response with the limited resources of the surrounding area. This mathematical model can be used to determine the best response decisions, such as where to send casualties and when to treat them. This model is also used to explore ideas of fairness and equity in casualty outcomes and examine what may lead in unfair response decisions. Finally, this dissertation uses a simulation to understand how this model could be used to not only plan the response, but also update the plan as you learn new information during the response roll-out.

This thesis looks at a variety of stakeholders and how they inform the conversations around disasters and disaster sites. In particular it focuses on the way knowledge frameworks of different actors informs this dialogue and defines the nature of their response. The thesis argues that this has an implication for debates on democracy, governance and citizenship. The thesis looks at four sets of actors: individuals confronting and coping with the everydayness of disasters.; the states of Gujarat and Orissa in India which innovate in the face of disasters to either create a techno-managerial response and institute different methodologies or use the existing structures to embed themselves further and perpetuate the poverty and disaster industry; the Christian and secular humanitarian groups: the former make a transition from charity to rights discourse while intervening in disasters. The latter focus on building methodologies which institute certain norms of responding to disasters and catering to those it considers as more vulnerable when disaster strikes. The thesis finally turns its attention to the response of four Hindu groups who draw on civilizational categories to engage with issues of pain, suffering, healing. Each stakeholder, the thesis argues, in articulating its response to disasters, presents a 'counter model' or at least a complementary understanding of how to think and respond to disasters. This plurality of engagement by questioning the preconceived frameworks adds not just to the democratic imagination but also to the debates on what constitutes governance and citizenship. Methodologically, the thesis is an ethnographic exploration located in two sites in India: Gujarat and Orissa. It keeps storytelling, ethnography, analysis, policy documents together and tries to show that they become a weave in disaster studies.

For nearly 200 years, there has been a neighborhood in New Orleans, Louisiana named Milneburg, which has been constantly reimagined by its inhabitants and others. From its inception as a port of entry in 1832 until the 2011, it has been called a world-class resort, the poor-man's Riviera, a seedy red-light district, a cradle of jazz, a village, a swath of suburbia and a neighborhood. It has been destroyed eight times due to storms, fires, and civic or governmental neglect. Each time its residents have rebuilt it. In its last iteration as a post-Katrina neighborhood, the residents reestablished the Milneburg Neighborhood Association in order to define its boundaries, gain control of its redevelopment and restrict who lived there as well as what activities were permitted. This is a case study of the trajectory of Milneburg and the cultural adaptations of its residents to keep it distinct, vital and respectable.

Abstract Nurses constitute the largest group in the healthcare workforce and are called on to assist in emergencies such as disasters. Research has shown that professionals with higher levels of knowledge are more likely to respond to actual emergencies. Yet most hospital based nurses do not possess the skills needed for disaster response. The Basic Disaster Life Support (BDLS) course, with its comprehensive content, represents the gold standard for disaster education. Since confidence also plays a role in response, a tool to measure this variable could be useful. There were five purposes of this study: determine whether one teaching method (computer or classroom instructor-led) is superior over another for disaster education; evaluate how knowledge retention varies between instructional models; examine whether a correlation exists between self-efficacy and disaster knowledge; pilot a new instrument, Disaster Self-Efficacy Scale (DSES); complete psychometrics on the Basic Disaster Life Support exam. The study was an experimental pretest/posttest/follow-up with a single between-group factor (type of training with three levels) and three within-group factors measured at three intervals. The sample included 82 hospital-based nurses randomly assigned to a computer-based, instructor-led, or control group. A MANOVA and MANCOVA were conducted to evaluate group differences at three time intervals. Psychometric evaluation was conducted on both the BDLS and the piloted Disaster Self-Efficacy measures. The BDLS test was shown to be in need of revisions and updating. The DSES measure shows promise for determining disaster self-efficacy and may be useful to target training though it needs further validation. Learning results showed that when controlling for pretest differences, experimental groups had higher posttest BDLS and DSES scores than the control group but there was no difference between experimental groups. There was no difference between experimental groups for BDLS scores at follow-up. Conclusions were that training, regardless of how it was delivered, led to a dramatic increase in disaster knowledge and disaster self-efficacy; computer-based education is a feasible alternative to teaching BDLS; retention still poses a challenge for disaster education. Implications for nursing education and practice were identified. Future research should focus on further development and validation of the DSES and BDLS instruments.

The hazards literature has identified many factors as being influential in the decision making process during high risk, short-notice disasters. Risk perception and previous disaster experience are commonly identified as two of the more influential factors in this complex process. However, few studies adequately address the complex role(s) that these factors play in self-protective decision-making during successive high-risk events. In particular, the role of previous disaster experience during subsequent events is still a matter of considerable discussion and inconsistent findings. This thesis examines two events that occurred in August, 2011 in Goderich, Ontario: an F-3 tornado that struck the community on August 21st and a tornado warning that was posted for the region three days later on August 24th. This case study provided the opportunity to examine the roles of risk perception and previous disaster experience in the decision-making process during successive high-risk events. Semi-structured interviews (n=35) and close-ended questionnaires (n=268) were conducted to learn about the ways that individuals obtained and understood risk information, and to explore whether and how such information guided protective behaviors during the two events. The interviews were analyzed using thematic coding to identify response patterns, and the questionnaires were analyzed using IBM SPSS software. It was found that a sizable portion of the sample population took protective actions on August 24th in ways that were inconsistent with their actions on August 21st. Also, a significant portion of respondents chose not to take any form of protective action on August 24th despite having previously experienced the damaging tornado. The findings of this research suggest that the significance of previous disaster experience in the decision-making process is highly variable and context-dependent. A second significant research finding involves the impact of the tornado on the place attachments of Goderich residents. It was found that the disaster had significant impacts, both positive and negative, on participants' sense of place. These findings have implications for both short- and long-term disaster recovery.