Wildfire-UAVSim is a customizable wildfire tracking simulator that enables the evaluation of diverse adaptation strategies. Among its many configuration parameters, the forest area is customizable with different densities of vegetation, as well as fire and smoke dispersion patterns that are affected by factors such as wind, conforming different observability conditions. The configuration options of our simulator also allow to place a team of UAVs in charge of tracking the fire over the forest area. Wildfire-UAVSim provides a graphical web interface native from Mesa framework, executed by the simulator, in order to keep track of how the simulation evolves in time.
5 Python files compose the project structure, namely:
This python file holds the logic for managing elements such as Fire, Smoke, Wind and UAVs.
This python file holds the logic for managing the wildfire simulation, by utilizing elements from agents.py file.
This python file allows to execute the wildfire simulation built in widlfire_model.py file.
This python file holds the variables used to set the simulation execution configurations.
This python file contains a Mesa class, modified for making UAV observation areas visible on the graphical web interface. It is not really necessary to change this file.
In the following subsections, the installation process for executing the project will be explained.
The first step is to download and install Pycharm Community Edition IDE to easily run and set up the project and its dependencies. For Linux users (this project was initially tested on Ubuntu 22.04.2 LTS), they can use the snap command in cmd (pre-installed from Ubuntu 16.04 LTS and later) as a fast installation option. Users must execute the command sudo snap install pycharm-community --classic in cmd for installing Pycharm Community Edition.
Despite this project was initially tested on Ubuntu 22.04.2 LTS, it has been later tested on Windows and Mac too. For checking system requirements, and information about the installation process, please visit https://www.jetbrains.com/help/pycharm/installation-guide.html.
First, extract the Wildfire-UAVSim downloaded package in any folder. Second, open Pycharm by executing the command pycharm-community in cmd, or searching for the executable in the computer.
Then, the projects window should be opened. Next, the user has to click on Open, select the extracted project folder, and click OK. A window should appear to select between light editor, and project editor.
Select project editor. For openning the project more times, repeat same process.
Once the project is opened, some dependencies are necessary. To install them, first go to Settings > Project > Python Interpreter, then select the desired Python interpreter
for executing the project. As default /usr/bin/python3.10 should appear in the Python Interpreter: tab, which already contain some default dependencies if Ubuntu 22.04.2 LTS is installed. For other Python interpreters,
other dependencies may be needed to be installed. On the same Pycharm configuration window, click on + icon, and search for the following dependencies (the user should specify the same version as the one used when developing the project. Version can be specified by clicking on the "Specify version" checkbox):
- Mesa (v.1.2.1)
- numpy (v1.24.2)
- matplotlib (v3.7.1)
Once project is opened, and dependencies were installed, main.py can be executed by selecting the file, right mouse click, and clicking on Run 'main' (shortcut should be Ctrl+Mayus+F10).
A web page interface should appear, with the wildfire grid, and buttons for configuring the simulation.
When executing the project as explained above, a web page hosted in http://127.0.0.1:8521/ should appear in user's default browser. Port can be modified in main.py file if user has the default one already busy.
The relevant graphical interface elements are:
The grid with generated cells, with vegetation, fire, smoke, and UAVs, can be seen in the center of the screen.
The start button allows to run the simulation without stopping.
The step button allows to execute one time step at a time.
The reset button allows to execute the reset() method, inherited and overwritten from Mesa framework class mesa.Model, into WildFireModel class, inside widlfire_model.py file.
It is a slider that allows to set the frames per second (FPS) velocity for the graphical visualization of the simulation execution. Each frame corresponds to one time step. Its range goes from 1 to 20 FPS, taking into account that, counterintuitively, 0 FPS set the fastest FPS velocity. One reason why the simulation might seem not be playing fluently could be the setting of the FIRE_SPREAD_SPEED variable referenced below.
Indicates the current time step of the simulation.
Global variables are used in the project to configure different simulation executions. In the next subsections several global variables descriptions are shown, as well as many configuration examples for execution.
BATCH_SIZE: It establishes how long the simulation will run, in number of time steps.
WIDTH, HEIGHT: They set the grid size (forest area size) in cells.
BURNING_RATE: It sets the fuel decay speed in terms of time steps.
FIRE_SPREAD_SPEED: it sets how fast fire spreads to other cells, in terms of time steps.
FUEL_UPPER_LIMIT, FUEL_BOTTOM_LIMIT: They establish the maximum and
minimum amount of burnable fuel present in each cell, respectively.
DENSITY_PROB: It is a value in the range [0, 1] that establishes the
percentage of the grid covered by vegetation.
ACTIVATE_WIND: It sets whether the fire spread is influenced by wind.
FIXED_WIND: If it is active, then wind blows in the direction set by WIND_DIRECTION. If it is not, it means wind blows two directions, specified by FIRST_DIR and SECOND_DIR. Since wind can blow a direction stronger than the other one, FIRST_DIR_PROB establishes the wind first direction’s predominance.
PROBABILITY_MAP: If it is active, the probability of the fire to spread to each cell at all times can be visualized.
MU: It sets how strong wind blows with a value in the range [0, 1].
ACTIVATE_SMOKE: It sets whether smoke will be part of the simulation.
SMOKE_PRE_DISPELLING_COUNTER: It establishes how fast smoke appears after fire starts in a cell.
NUM_AGENTS: It establishes the amount of UAVs that will fly over the forest area (zero indicates the simulator will simulate only the wildfire spread).
N_ACTIONS: Specifies the number of possible actions each UAV can take when deciding on a move, which by default is set as [north, east, west, south].
UAV_OBSERVATION_RADIUS: It sets the observation radius—technically it is not a radius, since observed areas have square shapes.
SECURITY_DISTANCE: It establishes the minimum distance that UAVs should be separated from each other for avoiding collisions.
Six default examples of how different variables can be configured to develop distinct scenarios, can be seen below. All scenarios shown are captured in time step = 20, in different time steps scenarios might look different.
Before showing the examples, this section compiles all variables that can be set in common with all examples. The variables that were not mentioned can be set to their default value.
BATCH_SIZE = 90
WIDTH = 50, HEIGHT = 50
BURNING_RATE = 1
FIRE_SPREAD_SPEED = 2
FUEL_UPPER_LIMIT = 10, FUEL_BOTTOM_LIMIT = 7
DENSITY_PROB = 1
A scenario with no wind, smoke, or UAV, should appear.
NUM_AGENTS = 0
ACTIVATE_WIND = False
ACTIVATE_SMOKE = False
PROBABILITY_MAP = False
Concretely, a scenario with two weak wind components should appear, first with 50% of south component, and a second west component with 50%. In this scenario, neither smoke nor UAV should appear.
NUM_AGENTS = 0
ACTIVATE_WIND = True
ACTIVATE_SMOKE = False
PROBABILITY_MAP = False
FIXED_WIND = False
WIND_DIRECTION = 'south'
FIRST_DIR = 'south'
SECOND_DIR = 'west'
FIRST_DIR_PROB = 0.5
MU = 0.5
A scenario with strong windy conditions, blowing east, and late short-lasting smoke should appear. Remember that, since the dispelling counter for smoke is set in Smoke class by default, inside agents.py file, changes should be done to the self.dispelling_counter_start_value variable, inside __init()__ method (Smoke class). Keep also in mind that self.dispelling_counter_start_value + SMOKE_PRE_DISPELLING_COUNTER should be greater than the amount of fuel assigned to each cell (for taking less risks, compare to FUEL_UPPER_LIMIT, which is the maximum possible amount of fuel of each cell), in order to avoid situations in which smoke dissipates before the end of the cell’s burning process.
NUM_AGENTS = 0
ACTIVATE_WIND = True
ACTIVATE_SMOKE = True
PROBABILITY_MAP = False
FIXED_WIND = True
WIND_DIRECTION = 'east'
MU = 0.95
SMOKE_PRE_DISPELLING_COUNTER = 7
self.dispelling_counter_start_value = 4
A scenario with 2 UAV having small partial areas in normal conditions should appear.
NUM_AGENTS = 2
ACTIVATE_WIND = False
ACTIVATE_SMOKE = False
PROBABILITY_MAP = False
UAV_OBSERVATION_RADIUS = 3
A scenario with 3 UAV having big partial areas, with fast long-lasting smoke, should appear.
NUM_AGENTS = 3
ACTIVATE_WIND = False
ACTIVATE_SMOKE = True
PROBABILITY_MAP = False
SMOKE_PRE_DISPELLING_COUNTER = 2
self.dispelling_counter_start_value = 9
UAV_OBSERVATION_RADIUS = 12
A scenario with normal conditions should appear. Keep in mind that changing wind conditions will affect to the visualized probabilitites. Also, remember to set 0 UAV when showing the fire probability map.
NUM_AGENTS = 0
ACTIVATE_WIND = False
ACTIVATE_SMOKE = False
PROBABILITY_MAP = True