Dante Benjamin Matellini reports on his PhD project on developing a risk-based fire and rescue model for dwelling fires where the majority fire deaths occur each year.  

As we know, every year fires in the UK kill around 500 people and injures a further 14,000 (Communities and Local Government 2009). In England alone the total economic loss from fires including human factors amounted to £8.3 billion in 2008 (Communities and Local Government, 2011). To combat losses from fire, the UK government takes a series of actions which centre on the provision of fire and rescue services (FRSs); inherently their management and operations are vital for maintaining high standards of fire safety throughout the UK. However this is a hugely complex task due to the sheer number of different fire scenarios which can develop. Not only are there diverse types of locations, for example dwellings, public buildings, factories, etc., but there are also different circumstances within each type of location.

My PhD focuses on developing a risk-based fire and rescue model for dwelling fires which importantly, is where most fire deaths occur each year. There are a vast number of variables to consider when modelling dwellings, for example variations will arise in terms of geographical location, fire safety arrangements, characteristics of occupants, activities of occupants, among others. As for the occurrence of fire itself, each incident will be unique in terms of time of day, type of fire, state of occupants, fire cues, etc. What all these variations signify is that the potential magnitude of the next fire event and its consequences are generally unpredictable. Because of complicated scenarios, unpredictability of outcomes, and high frequency of incidents, FRSs have to be both capable and flexible in operation; however finding the optimal way of providing emergency cover and minimizing risk is a complicated task which often results in reasoning and decisions taking place under uncertainty.

Bayesian Network Model  

In order to diminish some of this uncertainty and improve confidence in decision making, an extensive four-part Bayesian Network (BN) model is developed focusing on dwelling fires within the UK. The intention is to model the sequence of events which may occur during a fire from ignition through to extinguishment with the objective of assessing, under specified conditions, fire safety at a given location; this should assist in determining what the most important safety issues are for the purpose of improving fire prevention and mitigating consequences in order to reduce fire risk.

The model itself is broken down into four interlinked parts:
Part I Initial fire development - designed to investigate the potential for human reaction given initial fire circumstances.
Part II Occupancy response and further fire development - focuses on occupant actions and fire growth/flashover given various occurrences.
Part III Advanced fire situation and consequences - investigates the possible fire outcomes based on fire development and intervention of FRSs.
Part IV Fire service response time - examines factors affecting FRS response time.

The model parts will function either individually or together as an integrated model. Within the project a management framework is also presented to demonstrate how the BN model could fit into the strategic management of FRSs and how it could link up with other tools and data collection programmes. The BN model may prove to be useful for strategic decision making within FRSs.


Communities and Local Government (2009). Fire statistics United Kingdom 2007. Available at: http://www.communities.gov.uk/ [Accessed, 22/10/2010].

Communities and Local Government (2011). The economic cost of fire: estimates for 2008. Available at: http://www.communities.gov.uk/ [Accessed, 01/12/2011].