#FireWeather. Historical Weather Data Clustering for Scenario-based Fire Simulation
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1 #FireWeather Historical Weather Data Clustering for Scenario-based Fire Simulation Alex Masarie, student, Colorado State University Dr Douglas Rideout, Dr Yu Wei, Colorado State University Dr Michael Bevers, Rocky Mountain Research Station North Central CSC Open Science Conference --- May 20, 2015
2 1 Problem Description MEDIA/stelprdb jpg s/uploads/0616fireline350jpg g/%5citems%5cfire_shelter_ne w_genjpg chnology/images/huge-stack-of-moneyjpg upload/cover-2pdf
3 2 Dynamic Aspect
4 3a Scenario Trees (Fire Area, Fire Perimeter) Unconditional Probability
5 3b Scenario Trees Representative scenarios with conditional probabilities Mild Moderate Extreme
6 4 Modeling /code/php/php-display-current-dateand-timephp Black Hills National Forest, LANDFIRE data
7 5a Forecast Errors w 1 w 2 w 3 w 4 w 5 (85 F, (86 F, (98 F, (50 F, (72 F, (84 F, 19 %, 21 %, 9 %, 100 %, 35 %, 18 %, 4 mph, 4 mph, 10 mph, 0 mph, 8 mph, 4 mph, 220 ) 198 ) 248 ) 97 ) 218 ) 42 ) Issues multivariate, correlations, subjectivity
8 5b Forecast Errors Issues multivariate, correlations, subjectivity
9 5c Forecast Errors Recall, the square root function is multi-valued One possible decision rule error RH i = RH 0 RH i RH 0 error wspd i = wspd i wspd 0 wspd 0 error RH i + error wspd i 0 d w i, f 0 error RH i + error wspd i < 0 d w i, f < 0 Wet and calm Dry and windy
10 6 Ordering Historical Weather Data w 1 w 2 w i Least severe fire weather Most similar to forecast Some comments on M Closest weather station to fire Logical records given ignition ie no snow in July! M ~ 1000 w M Most severe fire weather
11 7a Hierarchical Clustering w 1 w 1 w 2 Representative Scenario (1) w 2 (1) w Q(1) w Q(1) +1 w i (2) (K 1 ) w Q(1) +2 w Q(1) +Q (2) w M-Q(K1 ) Representative Scenario (2) Representative Scenario (K 1 ) w M w M
12 7b Hierarchical Clustering Representative Scenario (1,1) w 1 Representative Scenario (1,2) w 1 w 2 w i w M w 2 w Q(1) w Q(1) +1 w Q(1) +2 w Q(1) +Q (2) w M-Q(K1 ) w M Representative Scenario (1,K 2 ) Representative Scenario (2,1) Representative Scenario (2,2) Representative Scenario (2,K 2 ) Representative Scenario (K 1,1) Representative Scenario (K 1,2) Representative Scenario (K 1,K 2 )
13 7c Hierarchical Clustering Probabilities are derived from cluster sizes Conditionality is maintained because we cluster subsets
14 Wet cold front p (1) = 290 / 986 = 029 Lower temp, prevailing winds p (2) = 572 / 986 = 058 Higher temp, prevailing winds p (3) = 67 / 986 = 007 Dry cold front p (4) = 57 / 986 = 006
15 (a) (b) (c) (d) *Topographic shading (light source from northwest)
16 8 Fire Behavior Simulation
17 9a Support Management Decisions
18 9b Support Management Decisions
19 Thanks! Please let me know what you think!
20 Additional Slides
21 2) Problem Detail Given: - A fire on a landscape, Single ignition Multiple ignitions - Knowledge about how the fire behaves, Weather Fuels - A set of resources, Topography Type I & II crews Dozers Tractor Plows Aircraft - The costs of using the resources, Fixed Variable Environmental Opportunity - Information about how the resources perform Achieve some set of fire management goals Fire Triangle Line building rates Interactions with terrain Interactions with fire Minimize cost, put out fires, protect WUI, minimize burned area, resource benefit, reduce potential for extreme fires, decrease fire size, multiple objectives
22 3) The Fire-S stochastic program Minimize expected burned area Costs and budget Line building and containment Logical dispatch Initial deployment tracking for fixed cost payment Contain or escape
23 3) The FireS stochastic program A multistage stochastic program with variable recourse Objective function: Not realizable in this continuous form!
24 3) The Fire-S stochastic program An example of recourse (LINE BUILT) (BINARY CONTAIN) * (FIRE PERIMETER) Suppose LINE BUILT = 90 km 90% A (2,1) = 304 ha P (2,1) = 81 km FIRE PERIMETER = 81 km so BINARY CONTAIN = 1 (contained) A (2) = 57 ha P (2) = 35 km 10% A (2,2) = 401 ha P (2,2) = 99 km FIRE PERIMETER = 99 km so BINARY CONTAIN = 0 (uncontained)
25 9 (Proposed) Statistical Testing Two-sample Kolmogorov-Smirov Testing (multivariate approximation) H 0 : Sampled from same distribution vs H 1 : Sampled from different distributions max,,, = p p temp p RH p wspd p wdir
26 Discussion Specific questions? Advantages/disadvantages of stochastic approach Validity of cluster analysis on fire weather (are temp, RH, wind vector sufficient?) Singleton clusters Operational level version Flexible temporal nature of stages Pre-defined decisions (BHNF has run-cards based on ERC) Rolling planning horizon Multi-fire version
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