Statistical Parameter Estimation For A Cellular
Automata Wildfire Model Based On Satellite
Observations
Author(s)
E. Couce & W. Knorr
Abstract
The importance of understanding the impact of wildfires on natural ecosystems
has given rise to the development of realistic computer models for the simulation
of wildfires. Stochastic models based on simplified equations and local
interactions, such as Cellular Automata (CA) models, are particularly popular as
an alternative to more computationally demanding deterministic models.
However, the challenges associated with observing wildfires under natural
conditions, and the highly non-linear nature of fire spread makes it extremely
difficult to parameterize them. In this work we present a method for adjusting
the behaviour of one such CA model from the statistical analysis of satellite data
of more than 750,000 African wildfires detected in 2003. Statistical metrics are
developed to characterize agreement between model and satellite observations.
The average probability of fire transmission amongst cells and the spatial scale
of the model are adjusted so that maximum agreement is found between model
output and the observed extension and statistical distribution of the real fires.
While the results obtained are only valid for the particular CA model used and
within the geographical limits of the region studied, we believe the process could
be adapted to fine-tune and validate other CA models in regions where enough
fire observations are available.
Keywords: fire spread model, cellular automata, parameter estimation, African
savanna wildfires, satellite observations.
1 Introduction
The importance of wildfires for natural ecosystems, together with the socioeconomic
danger they represent, have lead to a great deal of effort invested in the
Keywords
fire spread model, cellular automata, parameter estimation, African
savanna wildfires, satellite observations
Related Book
Other papers in this volume
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