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FIRE SIGNATURE PREDICTION OR FIRE MODELING

The Wildland Fire Signature Prediction Method is an expert’s model for predicting wildland fire behavior. The method employs the principle of Occam’s Razor; “the model with the least assumptions is the best model.”

The assumptions made in other models are many whereas the Campbell Prediction System, (C.P.S.) are few. The Signature model is focused on when and where the fire behavior will change that is to become worse or easer. The method uses observations of the fire at the head, flanks and heel, determining the cause of the variations in intensity, and identifies where these variations will replicate on the fireground. The goal of the method is to identify where the fire can be successfully contained. This is called “The Threshold of Control.’

The next phase is to determine the tactics and the resource requirements needed to accomplish the task. The information is transmitted with phrases contained in the CPS text and or a map showing the potential trigger points of change, tracks the fire could take and where the fire will fall within the threshold of control. The strategy that assures proper use of this model is; Fight the fire when and where there is a good chance of success and do not send resources where the effort will fail.

Occam’s Razor

Occam's razor is especially important for universal models such as the ones developed in General Systems Theory, mathematics or philosophy, because there the subject domain is of an unlimited complexity. If one starts with too complicated foundations for a theory that potentially encompasses the universe, the chances of getting any manageable model are very slim indeed. Moreover, the principle is sometimes the only remaining guideline when entering domains of such a high level of abstraction that no concrete tests or observations can decide between rival models. In mathematical modelling of systems, the principle can be made more concrete in the form of the principle of uncertainty maximization: from your data, induce that model which minimizes the number of additional assumptions.

Doug Campbell

9/7/2000