Development of a Modelling Framework to Improve Mitigation Methods and Contingency Planning for Emergency Scenarios

Natural disasters are major events that cause adverse effects, through natural earth processes, which may be hydrological, geological or meteorological / climatological. There are no fixed set of likely impacts from any one natural disaster but the, potentially devastating, consequences are nearly always wide reaching for both individuals and communities as a whole.

Consequences can be categorised through the three pillars of sustainability; economic, social and environmental. For any given event, all three pillars will be affected. For example, there are financial obligations through the re-building of communities and the insurance claims made. There are also social issues regarding the fracturing of communities, the devastation caused by of the loss of life or property and/or the constant fear of a reoccurrence that remains with communities. Finally, there are environmental concerns from the loss of habitat, pollution and the risk of further damage to the environment during the aftermath of the event.

Despite these natural hazard events being unpredictable, it is possible to carry out Disaster Management or Emergency Management planning. These management techniques are concerned with the creation of plans and systems which aim to reduce community vulnerability to hazards and enable them to cope with the impacts of disasters. In order to develop effective plans, there is a requirement to model how these systems, and also individuals, behave in these events. This can be particularly challenging as the systems are not only complex, the events that need to be considered are rare but there is often a lack of knowledge surrounding the existing infrastructure before an event occurs.

The aim of this project will be to create a modelling framework tool, which will help management professionals develop and test their contingency plans for emergency scenarios. Combined within the agent based environment will be a more robust model of human behaviour. This will allow emergency planners to be better prepared, reduce the interruption after an event and potentially reduce the mitigation required beforehand.