Looking to improve efficiency and effectiveness in initial firefighting operations by means of modelling response scenarios with real-time data and providing a decision support tool for decision-makers.


Current first-attack response strategies lack optimal response modelling and decision support capabilities, resulting in suboptimal resource allocation, delayed response times, and increased risks during Extreme Wildfire Events. This limits the effectiveness of initial firefighting efforts and can lead to escalated fire behaviour and greater damage. 

Why the Problem exists?

The problem of inadequate first-attack response modelling and decision support arises from several factors. Firstly, the complexity and uncertainty of wildfire behaviour make it challenging to determine the most effective initial response actions and resource allocation. Additionally, limited real-time situational awareness, incomplete understanding of fire behaviour dynamics, and inadequate decision support tools hinder the ability of incident commanders to make informed and timely decisions during the critical early stages of wildfire incidents. Moreover, resource constraints, coordination challenges among multiple agencies, and variations in operational protocols further contribute to suboptimal first-attack response strategies. 

Looking for solutions that completely or partially solve the following:

  • Enable incident commanders to anticipate fire spread, intensity, and potential impacts to inform initial response strategies. 
  • Consider factors such as fire behaviour predictions, available resources, response times, and operational constraints to determine the most effective distribution of personnel, equipment, and aircraft for initial fire suppression. 
  • Guide decision-making and prioritise response actions based on potential impacts, safety considerations, and resource availability. 
  • Integrate an exposure map and/or real time information on areas at risk to identify priority defending zones into the simulation tools to aid firefighting strategies and resource allocation. 
  • Facilitate information sharing, situational awareness, and coordination through integrated communication platforms, common operating pictures, and collaborative decision-making tools. 


  • Ensure seamless data integration to provide up-to-date information for accurate response planning and resource allocation. 
  • Account for variations in fire behaviour, resource availability, and incident complexity to ensure the scalability and adaptability of the modelling and decision support systems. 
  • Real-time situational awareness and interactive features to support decision-making during time-critical situations. 
  • Comprehensive training materials, simulations, and exercises to enhance their understanding and proficiency in utilising the tools effectively. 


  • Constraints due to data availability, quality, and consistency. 
  • Limited real-time data sources or subject to uncertainties. 
  • Accuracy and reliability of inputs used for real-time updating. 
  • Human resources and capacity building. 
  • Financial limitations. 

Fire Management Phase(s)

Detection & Response

Living Labs

Portugal Living LabGreece Living Lab; Norway-Sweden Living Lab; Chile Living Lab; Nouvelle Aquitaine – France Living Lab. 

Voice of the Living Lab(s)

  “To develop a methodology through the systematisation/ standardisation of environmental factors, initial attack procedures (in a spatiotemporal context) and suppression, taking into consideration the history of wildfires and the specificities of Greece. This methodology will define areas that require a different approach to initial attack and extinguishing means, in order to increase suppression efficiencies. Also, it will contribute to less empirical suppression operations.
– Establishment of objective indicators for the evaluation of the suppression operations.
– Fire ignition risk and spread indices with greater spatial resolution, possibly linked to the ENGAGE Fire Service operational system.
– Capability for online and real-time detailed cartographic imaging of the fire front (e.g. with thermal cameras) and transmission to the Coordination Center.
– Dense and well-maintained road network.
– A denser network of meteorological stations linked to the ENGAGE operational system. Possibility to access existing weather stations provided by other agencies (e.g. from the National Observatory of Athens).
– Suggesting that a meteorological station be installed in each Fire Station to enable simulations to be carried out or to create a meteorological database at the level of the Prefectural Administration.
– Provide a portable meteorological station at the incident site, close to the operational base, to record real-time meteorological data (microclimate parameters) and contribute to any fire behavior simulation.
– Personnel trained in new technologies”.

Greece Living Lab

  “The complexity of fire behaviour, which depends on many variables, and the lack of reliable real-time data due to insufficient human resources for data collection. Developing a system that accurately simulates fire behaviour (EWE), including suppression actions and realistic conditions that reflect the evolution of real fires“.

Portugal Living Lab


There is a lack of coordination of all the available data, modelling of the fire and modelling of fire behavior under different conditions and actions. Lack of data and model parameters for Nordic conditions. Incident commander tools 
• Software tool for analysis of factors at the incident location 
• Tool for modeling expected fire behavior, days/hours 
• Modeling worst case scenarios 
• Modeling effect of actions on the development of the fire 
• Models should incorporate the fires own effects on the local climate and further fire development“.

Norway-Sweden Living Lab



The problem exists due to a lack of planning and a lack of technical analysis of fires, the political and the technical do not talk or do little. Furthermore, there are no political priorities to do so. Today there is an inefficient management of resources by not establishing strategic and tactical objectives based on analysis of the forest fire“.

Chile Living Lab



An exposure map integrated to fire prediction modelling would enable firefighters to react efficiently to the priority defence areas (campsites, housing…) and therefore make it possible to provide resources in a limited timeframe“.

Nouvelle Aquitaine – France Living Lab


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