The Extended Attack Assessment index (EAA) was developed mainly for fire agencies because it includes pyroconvection effects to analyze fire spread after the initial response. This index helps agencies communicate wildfire potential clearly, promoting safe and reliable operations.
The EAA relies on parameters such as fuels, drought, meteorology, fuel physiological responses to environmental conditions, and atmospheric instability or the likelihood of convective processes. Technosylva uses a machine learning approach to analyze each variable's contribution to the EAA and improve the model's performance.