In Spain, the established fire control policy states that all fires must be controlled
and put out as soon as possible. Though budgets have not restricted
operations until recently, we still experience large fires and we often face
multiple-fire situations. Furthermore, fire conditions are expected
to worsen
in the future and budgets are expected to drop. To optimize the deployment
of firefighting resources, we must gain insights into the factors affecting how
it is conducted. We analyzed the national data base of historical fire records in
Spain for patterns of deployment of fire suppression resources for large fires.
We used artificial neural networks to model the relationships between the
daily fire load, fire duration, fire type, fire size and response time, and the
personnel and terrestrial and aerial units deployed for each fire in the period
1998-2008. Most of the models highlighted the positive correlation of burned
area and fire duration with the number of resources assigned to each fire and
some highlighted the negative influence of daily fire load. We found evidence
suggesting that firefighting resources in Spain may already be under duress in
their compliance with Spain’s current full suppression policy.
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