"The need obliged us" : culture as capacity during the Hurricane Stan emergency response. A case study from Tectitan, Huehuetenango, Guatemala

A major deficiency in disaster management plans is the assumption that pre-disaster civil-society does not have the capacity to respond effectively during crises. Following from this assumption a dominant emergency management strategy is to replace weak civil-society organizations with specialized disaster organizations that are often either military or Para-military and seek to centralize decision-making. Many criticisms have been made of this approach, but few specifically addresses disasters in the developing world. Disasters in the developing world present unique problems not seen in the developed world because they often occur in the context of compromised governments, and marginalized populations. In this context it is often community members themselves who possess the greatest capacity to respond to disasters. This paper focuses on the capacity of community groups to respond to disaster in a small town in rural Guatemala. Key informant interviews and ethnographic observations are used to reconstruct the community response to the disaster instigated by Hurricane Stan (2005) in the municipality of Tectitán in the Huehuetenango department. The interviews were analyzed using techniques adapted from grounded theory to construct a narrative of the events, and identify themes in the community’s disaster behavior. These themes are used to critique the emergency management plans advocated by the Guatemalan National Coordination for the Reduction of Disasters (CONRED). This paper argues that CONRED uncritically adopts emergency management strategies that do not account for the local realities in communities throughout Guatemala. The response in Tectitán was characterized by the formation of new organizations, whose actions and leadership structure were derived from “normal” or routine life. It was found that pre-existing social networks were resilient and easily re-oriented meet the novel needs of a crisis. New or emergent groups that formed during the disaster utilized social capital accrued by routine collective behavior, and employed organizational strategies derived from “normal” community relations. Based on the effectiveness of this response CONRED could improve its emergency planning on the local-level by utilizing the pre-existing community organizations rather than insisting that new disaster-specific organizations be formed.

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.