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Clustering Historical Rates of Spread
Stephen Guerin, SimTable
James Gattiker, LANL Statistical Sciences
May 20, 2014
What an Empirical Cluster @#$%!
Stephen Guerin, SimTable
James Gattiker, LANL Statistical Sciences
May 20, 2014
Silver Fire ROS vs Time for 11 Fuel Types
prog
ress
ion
rate
time
Dendrogram Clustering of 47 Fires
Example: Progression Rate, Fuel and Fire
Clustering from last slide
Orders the fire rate empirical pdf:
End
• 1 2• 2 33• 3 3• 4 29• 5 12• 6 28• 7 43• 8 23• 9 35• 10 17• 11 22• 12 45• 13 16• 14 6• 15 30• 16 32• 17 21• 18 44• 19 7• 20 11• 21 42• 22 19• 23 8• 24 9
• 25 46• 26 31• 27 37• 28 34• 29 38• 30 10• 31 20• 32 25• 33 41• 34 26• 35 18• 36 27• 37 4• 38 15• 39 39• 40 24• 41 36• 42 40• 43 5• 44 13• 45 1• 46 14• 47 47
Cluster Ordering
• 1 2• 2 33• 3 3• 4 29• 5 12• 6 28• 7 43• 8 23• 9 35• 10 17• 11 22• 12 45• 13 16• 14 6• 15 30• 16 32• 17 21• 18 44• 19 7• 20 11• 21 42• 22 19• 23 8• 24 9
• 25 46• 26 31• 27 37• 28 34• 29 38• 30 10• 31 20• 32 25• 33 41• 34 26• 35 18• 36 27• 37 4• 38 15• 39 39• 40 24• 41 36• 42 40• 43 5• 44 13• 45 1• 46 14• 47 47
Cluster Ordering
Using Knowledge from Observed Wildfires
Historical analysis / fire-ography (pyronography :-)• Training and Education
– Use past fire data to explore fire characteristics and response– Demonstrate the variability of fire qualities and fire conditions
• Expectations of fire behavior• Community decisions on defensible spaces
• Fire situation awareness– Query historical database of fire observations for likely analogs during
an unfolding wildfire event– Create empirical models; aggregate characteristics of fire types
• Model Calibration– Resource for tuning model parameters, and analyzing the possible
failures (and quantified uncertainty) of model predictions