1. A Cellular Automata Wildfire Spread Model
PILOT Presentation
Kristen J. Bains
November 6, 2006

2. Model Goals To use a cellular automaton to model wildfire
To create an extensible framework
To demonstrate spatial control

3. Implementation Overview
GUI: Java 1.5.0
Swing allows easy GUI design and layout
JAI 1.1.2
Model: C++
OOP allows easy extensibility

4. GUI Design
Image Area
Menu Bar
Control Panel

5. Walk-through : GUI
Load landscape file

6. Walk-through : GUI
Load landscape file
FileDialog

7. Walk-through : GUI Load landscape file FileDialog
Java Advanced Imaging (JAI) reads images/landscape_1.bmp and draws to image area

8. Walk-through : GUI Load landscape file FileDialog
Java Advanced Imaging (JAI) reads images/landscape_1.bmp and draws to image area
Writes data/whichImage.dat & data/whichLandscape.dat
Talk about how data is passed between GUI and MODEL

9. Walk-through : GUI After the file is loaded.
Talk about how the landscape is generated.

10. Landscape Files Randomly generated
libnoise (
noiseutils renders image

11. Landscape Files Randomly generated
libnoise (
noiseutils renders image
Perlin noise module
Source:

12. Landscape Files Create your own Set parameters Bounding Box Rows/Cols
Filename

13. Landscape Files Create your own Set parameters
File → Create new landscape file

14. Landscape Files Create your own Set parameters
File → Create new landscape file
Invokes landscape/generateMap
C++ routines using libnoise and noiseutils
Creates images/landscape_new.bmp & data/landscape_new.dat
Loads file into image area of GUI
Explain what is contained in landscape_new.dat

15. Walk-through : GUI Select a firebreak
Creates a list of x,y coordinates that is stored to an array.

16. Walk-through : GUI
Select a fire start location

17. After firebreak and fire start selection
Walk-through : GUI
After firebreak and fire start selection

18. Walk-through: GUI Run → Run the model Writes initialization file
Writes firebreaks & fire starts to separate files
Invokes fire readState data/init.dat

19. Walk-through: Model Two classes
CACell
CASpace
main parses initialization file, sets global variables
initialize()
initializes CASpace
creates vector< vector<CACell> >

20. Walk-through: Model startFire()
looks at fire starts & sets their burnState
adds fire starts to burnList

21. Walk-through: Model go()
iterates through burnList (all currently burning cells)
fire spreads from burning cells
newly burning cells are added to end of burnList
done when burnList is empty

22. Spread
if(fuelDensity > Threshold1)
cell can burn

23. Spread if (fuelDensity > Threshold1) if (usingMultipleTimeSteps)
cell can burn
if (usingMultipleTimeSteps)
if (fuelDensity > Threshold2)
cell can burn 2 time steps

24. Spread
8-neighborhood (3x3)
24-neighborhood (5x5)

25. Walk-through: GUI
Final burn state information located in data/final.dat
After the model has finished running, the GUI regains control and displays the burned cells in the image area.

26. Walk-through: Optimization
Select fire start
location
Select firebreak
start location
Select orientation
Select % map
to search
Popup window
to select name

27. Walk-through: Optimization
Run → Run optimization
Writes initialization file
Optimization flag set
Writes firebreak location & fire start to separate files
Invokes fire readState data/init.dat

28. Walk-through: Optimization
main parses initialization file, sets global variables
initialize() initailizes CASpace
startFire() gets start location

29. Walk-through: Optimization
go() burns the fire to completion
toList is created while burning
cell (x, y)’s toList contains: 1, 3, 4, 7
Baseline numberBurned

30. Walk-through: Optimization
For each selected orientation
lengthen the firebreak
width = 1

31. Walk-through: Optimization
width = 2
Growth direction
for width

32. Walk-through: Optimization
Reverse Fire

33. Walk-through: Optimization
Reverse Fire

34. Walk-through: Optimization
Reverse Fire

35. Walk-through: Optimization
Reverse Fire

36. Walk-through: Optimization
Reverse Fire

37. Walk-through: Optimization
Reverse Fire

38. Walk-through: Optimization
Reverse Fire

39. Walk-through: Optimization
Reverse Fire

40. Walk-through: Optimization
Reverse Fire
We know that this firebreak length is optimal because it results in the fewest burned cells

41. Walk-through: Optimization
Output is written to data/*.opt file
Best firebreak is calculated and shown on GUI

42. Example Output

43. Example Output

44. Future Directions
Implement in OpenInventor (
Customizable 3D simulation environment, C++
Implement interface with ArcGIS
Read ascii grid files and convert to array of float values

45. Future Directions
Extend with new modules
wind
slope
fuel moisture

46. Survey of Known CA Wildfire Models
Dunn and Milne. Modelling Wildfire Dynamics via Interacting Automata. *
Uses Circal process algebra to interact between automata
Clarke, Riggan, and Brass. A cellular automaton model of wildfire propagation and extinction.
Links remotely sensed data with Monte Carlo based simulation
Berjak and Hearne. An improved cellular automaton model for simulating fire in a spatially heterogeneous Savanna system.
CA approach based on Rothermel model
Most CA wildfire models do not contain control methods or an attempt to optimize them

47. Acknowledgements Funding under NSF Award No. IIS-0427471 Committee:
Dr. David Banks
Dr. Michael Berry, chair
Dr. Louis Gross
TIEM Group