Wildfire simulation using paralleled cellular automaton

Environment Specification

In order to achieve best compatibility, this program only needs very basic prerequisite.

1. Openmpi/3.0.0
2. Python/2.7.11 (and mpi4py package)

However, to generate a better visualization, this program needs additional packages.

1. numpy
2. matplotlib
   If the program failed to import these two packages, then it will automatically run in pure text mode.

Usage

Easiest place to change

This program is carefully designed, so the places you can change is very few. The easiest part to change is surrounded by -----Quick Change-----. You can simply change:

Variable Name	Acceptable Value	Purpose
n_row_total	any positive integer	the total row of the grid
n_col	any positive integer	the total column of the grid
generation	any positive integer	the total number of iterations
p_continue_burn	between 0 and 1	the possibility one cell continues to burn in next time step
wind	True or False	Switch for wind factor in the model (True for on, False for off)
vegetation	True or False	Switch for vegetation factor in the model (True for on, False for off)
density	True or False	Switch for vegetation density factor in the model(True for on, False for off)
altitude	True or False	Switch for altitude factor in the model(True for on, False for off)

Or you can do some custom changes on the environment initial functions:

Function Name	Purpose	Output
thetas (a list)	the angle of wind and direction of fire spread	3x3 list
init_vegation	the matrix for vegetation type	n_colxn_row list
init_density	the matrix for vegetation density	n_colxn_row list
init_altitude	the matrix for altitude	n_colxn_row list
init_forest	the matrix for initial forest condition	n_colxn_row list

Please don't change anything below the line Do not change anything below this line

Run the code:

After loading the module, you can use mpirun -n [n] python Parallel_Wild_Fire.py to run this code. You can specify the number of workers in n if you have multiple CPU.

Maths Model

The model we use here is based on the research paper: "A cellular automata model for forest fire spread prediction: The case of the wildfire that swept through Spetses Island in 1990"

Each cell has 4 states: State = 1: The cell contains no forest fuel. This state may describe the cells corresponding to parts of the city with no vegetation, rural areas with no vegetation etc. We assume that cells that are in this state cannot be burned. State = 2: The cell contains forest fuel that has not ignited. State = 3: The cell contains forest fuel that is burning. State = 4: The cell contained forest fuel that has been burned down.

These 4 states changes under these 4 rules: Rule 1: IF state (i,j,t) = 1 THEN state (i,j,t + 1) = 1. This rule implies that the state of a cell with no forest fuel (empty cell) remains the same and thus it cannot catch fire. Rule 2: IF state (i,j,t) = 3 THEN state (i,j,t + 1) = 4. This rule implies that a burning cell at the current time step will be burned down at the next time step. Rule 3: IF state (i,j,t) = 4 THEN state (i,j,t + 1) = 4. This rule implies that the state of an empty cell that has been burned down in the previous step stays the same. Rule 4: IF state (i,j,t) = 3 THEN state (i ± 1, j ± 1, t + 1) = 3 with a probability pburn. I changed Rule to IF state (i,j,t) = 3 THEN state (i,j,t + 1) = 4 with probability 1-p_continue_burn

And we used the following formulas to determine p_burn: p_burn=p_h(1+p_veg)(1+p_den)p_wp_s
**Effect of the wind speed and direction ** p_w = exp(c₁V)f_t,
f_t = exp(Vc₂(cos(θ)-1))
θ is the angle between the direction of the fire propagation and the direction of the wind.
**Effect of the ground elevation ** P_s = exp(aθ_s)
θ_s = tan-1[(E₁-E₂)/l]

Table for p_den:

Category	Density	pden
1	Sparse	-0.4
2	Normal	0
3	Dense	0.3

Table for p_veg

Category	Type	pveg
1	Argicultural	-0.3
2	Thickets	0
3	Hallepo-pine	0.4

Other parameters

Parameter	Value
ph	0.58
a	0.078
c1	0.045
c2	0.131

Implementation

Parallel Version