Automatic Calibration of Forest Fire Weather Index For Independent Customizable Regions Based on Historical Records

Wildfire Decision Support Systems are critical tools for civil protection authorities in the management of all wildfire stages, including prevention. To timely act and apply the necessary preventive measures to reduce the fire danger in wildfires, many proposed calibration studies of the Canadian Forest Fire Weather Index System (CFFWIS) have been performed mainly based on techniques that still depend on manual and empirical analysis, being limited to exploiting a few regions. This paper proposes a methodology for automatic calibration of the CFFWIS to obtain a fire danger measurement that best suits the specific characteristics of a given region. The proposed methodology, applied to 769 regions from Europe, is based on the k-means clustering technique to automatically identify patterns in the data sets composed of elements of the CFFWIS and wildfire records. The results of the automatic calibration of the CFFWIS on each of the 769 regions reinforce the versatility of the proposed methodology, which can be adapted to different regions.