Fire safety management based on integrated monitoring and forecast of smoke exposure

Abstract

Decisions made by firefighters during suppression operations are highly dependent on personal judgement, experience, and senses. However, recent scientific and technological advances offer a vast number of possibilities towards advanced emergency preparedness during firefighting operations, if integrated in a single on-line platform. In this context, this paper addresses the following question: can models be successfully merged with personal sensors aiming to build an advanced Decision Support System (DSS) focused on the preservation of firefighter’s safety, while pursuing an enhanced response to wildfires? The goal of this research is to develop an emergency response support system for firefighting ground-based operations during forest fire events. The target of the under-development computational tool is to provide knowledge-based aid to firefighters at critical decision-making situations, helping with the safe and successful management of fire. This DSS will support the following features: - capture of ‘live’ sensor data from a set of wearable monitoring equipment that will be based on a previously developed certified medical wearable technology named “VitalJacket®”; - capture of meteorological observations from a mast placed in one of the vehicles; - enable projections of fire progression, smoke levels and critical exposure in a predefined time-step (up to 30 minutes); - interpretation of results in an easy and intuitive form to be used by fire managers or firefighters involved in operations. To achieve this goal hardware technology (e.g. wearable, mobile, communications) was combined with processing/simulation software algorithms that run under a user interactive interface. A data assimilation technique will allow near real-time observations from wearable monitoring equipment to be integrated into the exposure forecast modelling system, increasing the accuracy of the estimates. A first prototype of the DSS, running in off-line mode, will be tested in the terrain during the fire season of 2014. An improved online version of the prototype will be tested in the following autumn in a series of prescribed burns. In conclusion, this work proposes the development and testing (under real conditions) of a DSS intended to provide optimized firefighting efficiency, enhanced hazard awareness, and knowledge-based response to forest fire events. Advances in the computational modelling of fire and smoke behaviour, in conjunction with personal monitoring data, provide near real-time simulation of local fire conditions and short-term smoke exposure forecasts, with the needed advance in time to permit the safe and efficient positioning of crews in the terrain.