CS382:Fire

Fire

Overview

This short unit about the spreading of forest fires is intended to teach some of the basics of using a simple pre-made model/simulation. While there are many benefits to using this model, the ability to physically verify the results proves to be difficult. It turns out that the rudimentary simulation of a wild fire spreading through a forest of varying densities can be implemented in a wide range of tools including NetLogo, AgentSheets, Vensim, Excel, and possibly others. Thus, this single model can teach the basics of simulation techniques like agent modeling, cellular automata, and systems dynamics without requiring students to relearn or rediscover what results to expect and allows them to focus on the methods and the techniques.

Reference Material

WildFire Modeling (wikipedia)

• Geared a little bit towards the teachers and upper end students. Meant largerly as an intro into what we're looking at,

Agent-based modeling and simulation of wildland fire suppression

• Much more so geared towards the teachers. This is a fairly technical article and is meant to give an overview of an Agent-Based version of wildfires.

Cellular Automata (wikipedia)

• Parts of this article are fairly technical and mathematical, however I think there's a lot of good information here. Perhaps we could write up a summary.

History of Cellular Automata

Lecture Notes

Lecture 1

• Brief cover of wildfires, to understand the basics of what we're going to try to model
  • Fires can start any number of ways (lightning, careless smokers, etc.)
  • Fires can spread in many ways (more lightning, wind, dense undergrowth, etc.)

• Start covering basic dynamic modeling methods (brief overview, we'll cover Cellular Automata later)
  • Cellular Automata
    • Cells of a grid can be in some state
      • Think of a sheet of graph paper and you can either shade in a square or not
    • One cell's state may or may not affect its neighbors
    • Changes based on a set of rules
  • Agent Modeling
    • Independent agents whose behavior is governed by sets of rules
  • Systems Dynamics
    • Sets of math equations govern the output of a set of graphs
    • Output of equations is governed by rates

Lecture 2

• More in-depth coverage of cellular automata
• Game Of Life - canonical example [GoL on Wikipedia]
  • Grid where each cell can either be alive or dead
    • Cells can either be alive or dead based on the number of living/dead cells around them
  • This rule controls the entire simulation and can produce some seemingly complex results
• The wildfire model we'll use is a special-case of the Game of Life
  • Trees can either be alive, burning, or smoldering
    • Alive trees can be caught on fire by neighboring burning trees and become burning
    • Burning trees can catch neighboring alive trees on fire and can become smoldering after a certain time
    • Smoldering trees can do nothing
  • In the basic model, the average distance between trees (i.e. the forest's density) can be controlled
  • Certain aspects can be seen by varying this control
    • Lightning: where new fires can be started at a random location
    • Wind: where fire has a tendency to spread in certain directions
    • Wetness: where certain trees may have a decreased chance to catch on fire
    • Duration of Burning: where burning trees may stay burning longer
  • As with density, tuning these "knobs" can produce different behaviors

Lab

This lab will consist of learning how to use NetLogo's wildfire model to see how minor changes in parameters can, under certain circumstances, producing wildly different results.

The student should first see the tediousness of the process of:

1. Set the desired parameter to some value
2. Run the model
3. Record the proper results into a spreadsheet
4. Increment the parameter and repeat steps (2-4)

The next step is to learn how to use NetLogo's parameter sweep ("Behavior Space") functionality to automate this process.

Ideally, when they run the manual parameter sweep they'll get results that tell them very little about how the density of the affects how much of it gets burnt. This will stress the importance of taking representative data sets to be able to accurately analyze the model.

A continuation of the lab would be to use one of the extended models (likely written by one of the TAs) and run parameter sweeps to understand how the different features can dramatically change the results.

Software

• [NetLogo] version 4.0

Bill of Materials

As long as the students don't try to actually burn down a forest to validate these models, there is no cost for this lab.

Evaluation

CRS Questions

• Which of these is a reasonable method for simulating Wild Fires?

1. A technique called "systematic dynamical conflagration"
2. Going out back campus and ....
3. Coding all the properties of wood into a program
4. A technique called "cellular automata"

• What is another name for "Cellular Automata"?

1. Automated Telecomune
2. Tessellation Automata
3. Biological Automated Simulation
4. Systems Dynamics

• Who is credited for doing some of the first work in Cellular Automata?

1. Stephen Wolfram
2. John von Neumann
3. Alan Turing
4. Stanislaw Ulam

Quiz Questions

• A question.

Fire Metadata

This section contains information about the goals of the unit and the approaches taken to meet them.

Scheduling

This should be very early in the semester as it is a fairly simple and short topic. Given its simplicity, it should only be a single week.

Concepts and Techniques

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General Education Alignment

• Analytical Reasoning Requirement
  • Abstract Reasoning - From the [Catalog Description] Courses qualifying for credit in Abstract Reasoning typically share these characteristics:
    • They focus substantially on properties of classes of abstract models and operations that apply to them.

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• • • They provide experience in generalizing from specific instances to appropriate classes of abstract models.

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• • • They provide experience in solving concrete problems by a process of abstraction and manipulation at the abstract level. Typically this experience is provided by word problems which require students to formalize real-world problems in abstract terms, to solve them with techniques that apply at that abstract level, and to convert the solutions back into concrete results.

**** Analysis of this unit's support or not for this item.

• • Quantitative Reasoning - From the [Catalog Description] General Education courses in Quantitative Reasoning foster students' abilities to generate, interpret and evaluate quantitative information. In particular, Quantitative Reasoning courses help students develop abilities in such areas as:
    • Using and interpreting formulas, graphs and tables.

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• • • Representing mathematical ideas symbolically, graphically, numerically and verbally.

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• • • Using mathematical and statistical ideas to solve problems in a variety of contexts.

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• • • Using simple models such as linear dependence, exponential growth or decay, or normal distribution.

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• • • Understanding basic statistical ideas such as averages, variability and probability.

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• • • Making estimates and checking the reasonableness of answers.

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• • • Recognizing the limitations of mathematical and statistical methods.

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• Scientific Inquiry Requirement - From the [Catalog Description] Scientific inquiry:
  • Develops students' understanding of the natural world.

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• • Strengthens students' knowledge of the scientific way of knowing — the use of systematic observation and experimentation to develop theories and test hypotheses.

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• • Emphasizes and provides first-hand experience with both theoretical analysis and the collection of empirical data.

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Scaffolded Learning

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Inquiry Based Learning

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