Abstract
Natural and technological disasters have been part of the daily life of societies in recent decades, causing harm and disruption in different parts of the world where they occur. Emergency management is the discipline that aims to promote support to the populations involved in a disaster, in order to mitigate the consequences of such disaster. Modelling and simulation plays a key role in decision-making and training in face of complex systems and procedures. Organizations responsible for responding to different types of disaster need tools that can improve the training and preparation of disaster support teams, creating scenarios as close to reality as possible. This chapter reports the creation of a solution for a scenario generation system capable of producing events similar to those verified in disasters, with a view to conducting training sessions, including near-real-time tabletop exercises and the planning and execution of field exercises, with the aim of decision-making training for relief teams in emergency situations.
TopMethodology
The Design Science Research (DSR) methodology was chosen as the research approach for this research (Simon, 1996; Van Aken, 2004). The choice is supported by the need for conducting research through a systematic set of steps (shown in in Figure 1) for the design, construction, and validation an artifact that addresses a defined practical problem (Vaishnavi & Kuechler, 2004). This work focuses on two of the steps of the process: (1) suggestion, whose scope and aim is to understand and study the context of the problem, and; (2) development, a step concerning the building of the proposed artifact for the generation of synthetic events related to specific types of disasters. The steps of solution’s validation and implementation were not considered at the current stage of this research.
Figure 1. The Design Science Research (DSR) Methodology’s Main Steps
Key Terms in this Chapter
Modeling and Simulation (M&S): The discipline that comprises the development and/or use of models and simulations.
Continuous Model: A mathematical or computational model whose output variables change in a continuous manner. Contrast with discrete model.
Asset: A collection of associated artifacts that together composes a system or subsystem. May exist in two types: resource asset and support asset.
Architecture: The structure of components in a system, their interrelationships, principles, and guidelines governing their design and evolution over time. artificial intelligence. Intelligence as exhibited by a man-made, non-natural, or manufactured entity.
Simulation Application: The executing software on a host computer that models all or part of the representation of one or more simulation entities. The simulation application represents, or simulates, real-world phenomena for the purpose of training, analysis, or experimentation.
Database Management System: A system that provides the functionality to support the creation, access, maintenance, and control of databases, and that facilitates the execution of application programs using data from these databases.
Scenario: An initial set of conditions and timeline of significant events imposed on trainees to achieve exercise objectives.
Discrete Event Simulation: A simulation that uses a discrete model where the dependent variables (i.e., state indicators) change discretely at points in time referred to as events. Contrast with: continuous simulation.
Model: A physical, mathematical, or otherwise logical representation of a system, entity, phenomenon, or process.
Algorithm: A prescribed set of well-defined unambiguous rules or processes for the solution of a problem in a finite number of steps.
Taxonomy: A classification system. Provides the basis for organizing objects for identification, retrieval and research purposes.