Wildfire Decision Support Systems (DSS) are critical tools for civil protection authorities in the management of all wildfire stages, including prevention, planning and combat. They enable the identification of areas prone to wildfire occurrences and can quantify the fire-risk associated. This allows to timely act and apply the necessary preventive measures to reduce the fire danger, in the short and long terms. During a wildfire event, DSS can be used to simulate and predict the fire behavior, allowing the planning and pre-positioning of the firefighting teams, the evacuation of the population from the affected areas, the closing of roads and railways, and the activation of backup systems for the affected infrastructures, including energy, water, communications, and transportation.

The fire behavior simulation tools can also be used to predict the effectiveness of fire suppression actions, to better coordinate and plan such efforts, as part of a risk governance system for wildfires.

The IMfire project proposes the development of a DSS platform capable of improving the current state-of-the-art DSS for wildfires, addressing some of their problems or limitations, as shall be explained next.

Tailor-made wildfire DSS are being used operationally for many regions around the world, for example, the Wildland Fire Decision Support System (WFDSS), built and used in the US, the Canadian Wildland Fire Information System (CWFIS) to support forest fire management across Canada, the Sentinel, used in Australia or the AFIS, used in South Africa. The advantage of having a tailormade solution is that it is calibrated and suited for the specific characteristics of each region, in terms of orology, vegetation, climate, previous occurrences records, firefighting means and legislations, among other factors. However, in some regions, such as Portugal, there is no specific DSS which can perfectly translate the local conditions.