Toward a theory of meso-scale wildfire modeling: A complex systems approach using artificial neural networks

Modeling wildfire spread patterns is a complex problem involving long-term fuel accumulation (site history) with short-term thermodynamics. Two general approaches to modeling wildfire spread patterns are fine-scale mechanistic or broad-scale probabilistic. Mechanistic approaches scale locally (micro-theory) to what keeps a fire burning while fire spread in probabilistic models is constrained by the rate of percolation across the landscape (macro-theory). Changing spatial and temporal scales of fire environment variables lead to the inherent unpredictability found in middle number systems. Extant fire models lose predictive power when subtle shifts in environmental variables cause a qualitative change in fire behavior. Artificial neural networks (ANNs) are designed for problems with cross-scale relationships that produce non-linear changes in system behavior (meso-theory). Even though the system appears middle number, the ANN recasts system structure until, at an appropriate level of analysis, prediction becomes possible. The difficulty with ecological systems is they invite being cast as complex, and complex systems require different causal models. A systems approach incorporates the explanatory power of positive and negative feedbacks and the recognition of emergent system behavior. Because complex systems do not invite definitive answers, we need complex systems methodologies like ANNs to offer prediction with good explanatory power. An ANN wildfire spread model was developed that integrates across scales of fire environment variables. Preliminary results support the proposed meso-theoretical fire environment definition and ANN-based modeling approach.