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Fire Model Prob

dissmodel.models.ca.fire_model_prob.FireModelProb

Bases: CellularAutomaton

Probabilistic forest fire cellular automaton.

Extends the basic fire model with two stochastic processes:

  • Spontaneous combustion — a forest cell can catch fire randomly, even without burning neighbors.
  • Regrowth — a burned cell can recover and become forest again.

This creates a dynamic equilibrium between forest growth and fire, producing complex spatial patterns over time.

Parameters:

Name Type Description Default
gdf GeoDataFrame

GeoDataFrame with geometries and a state attribute.

 required
**kwargs Any

Extra keyword arguments forwarded to :class:~dissmodel.geo.CellularAutomaton.

 {}

Examples:

>>> from dissmodel.geo import regular_grid
>>> from dissmodel.core import Environment
>>> gdf = regular_grid(dimension=(10, 10), resolution=1, attrs={"state": 0})
>>> env = Environment(end_time=20)
>>> fire = FireModelProb(gdf=gdf)
Source code in dissmodel/models/ca/fire_model_prob.py 
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class FireModelProb(CellularAutomaton):
    """
    Probabilistic forest fire cellular automaton.

    Extends the basic fire model with two stochastic processes:

    - **Spontaneous combustion** — a forest cell can catch fire randomly,
      even without burning neighbors.
    - **Regrowth** — a burned cell can recover and become forest again.

    This creates a dynamic equilibrium between forest growth and fire,
    producing complex spatial patterns over time.

    Parameters
    ----------
    gdf : geopandas.GeoDataFrame
        GeoDataFrame with geometries and a ``state`` attribute.
    **kwargs :
        Extra keyword arguments forwarded to
        :class:`~dissmodel.geo.CellularAutomaton`.

    Examples
    --------
    >>> from dissmodel.geo import regular_grid
    >>> from dissmodel.core import Environment
    >>> gdf = regular_grid(dimension=(10, 10), resolution=1, attrs={"state": 0})
    >>> env = Environment(end_time=20)
    >>> fire = FireModelProb(gdf=gdf)
    """

    #: Probability of a forest cell spontaneously catching fire each step.
    prob_combustion: float

    #: Probability of a burned cell regrowing as forest each step.
    prob_regrowth: float

    def setup(
        self,
        prob_combustion: float = 0.001,
        prob_regrowth: float = 0.1,
    ) -> None:
        """
        Configure the model and build the neighborhood.

        Parameters
        ----------
        prob_combustion : float, optional
            Probability of spontaneous combustion per step, by default 0.001.
        prob_regrowth : float, optional
            Probability of regrowth from burned to forest per step,
            by default 0.1.
        """
        self.prob_combustion = prob_combustion
        self.prob_regrowth = prob_regrowth
        self.create_neighborhood(strategy=Queen, use_index=True)


    def rule(self, idx: Any) -> int:
        """
        Apply the probabilistic fire transition rule to cell ``idx``.

        Parameters
        ----------
        idx : any
            Index of the cell being evaluated.

        Returns
        -------
        int
            New state for the cell:

            - Forest → ``BURNING`` if a neighbor is burning, or with
              probability :attr:`prob_combustion`.
            - Forest → ``FOREST`` otherwise.
            - Burning → ``BURNED`` always.
            - Burned → ``FOREST`` with probability :attr:`prob_regrowth`,
              or ``BURNED`` otherwise.
        """
        state = self.gdf.loc[idx, self.state_attr]

        if state == FireState.FOREST:
            if (self.neighbor_values(idx, self.state_attr) == FireState.BURNING).any():
                return FireState.BURNING
            return (
                FireState.BURNING
                if random.random() <= self.prob_combustion
                else FireState.FOREST
            )

        if state == FireState.BURNING:
            return FireState.BURNED

        # BURNED
        return (
            FireState.FOREST
            if random.random() <= self.prob_regrowth
            else FireState.BURNED
        )

rule(idx)

Apply the probabilistic fire transition rule to cell idx.

Parameters:

Name Type Description Default
idx any

Index of the cell being evaluated.

 required

Returns:

Type Description
int

New state for the cell:

  • Forest → BURNING if a neighbor is burning, or with probability :attr:prob_combustion.
  • Forest → FOREST otherwise.
  • Burning → BURNED always.
  • Burned → FOREST with probability :attr:prob_regrowth, or BURNED otherwise.
Source code in dissmodel/models/ca/fire_model_prob.py 
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def rule(self, idx: Any) -> int:
    """
    Apply the probabilistic fire transition rule to cell ``idx``.

    Parameters
    ----------
    idx : any
        Index of the cell being evaluated.

    Returns
    -------
    int
        New state for the cell:

        - Forest → ``BURNING`` if a neighbor is burning, or with
          probability :attr:`prob_combustion`.
        - Forest → ``FOREST`` otherwise.
        - Burning → ``BURNED`` always.
        - Burned → ``FOREST`` with probability :attr:`prob_regrowth`,
          or ``BURNED`` otherwise.
    """
    state = self.gdf.loc[idx, self.state_attr]

    if state == FireState.FOREST:
        if (self.neighbor_values(idx, self.state_attr) == FireState.BURNING).any():
            return FireState.BURNING
        return (
            FireState.BURNING
            if random.random() <= self.prob_combustion
            else FireState.FOREST
        )

    if state == FireState.BURNING:
        return FireState.BURNED

    # BURNED
    return (
        FireState.FOREST
        if random.random() <= self.prob_regrowth
        else FireState.BURNED
    )

setup(prob_combustion=0.001, prob_regrowth=0.1)

Configure the model and build the neighborhood.

Parameters:

Name Type Description Default
prob_combustion float

Probability of spontaneous combustion per step, by default 0.001.

 0.001
prob_regrowth float

Probability of regrowth from burned to forest per step, by default 0.1.

 0.1
Source code in dissmodel/models/ca/fire_model_prob.py 
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def setup(
    self,
    prob_combustion: float = 0.001,
    prob_regrowth: float = 0.1,
) -> None:
    """
    Configure the model and build the neighborhood.

    Parameters
    ----------
    prob_combustion : float, optional
        Probability of spontaneous combustion per step, by default 0.001.
    prob_regrowth : float, optional
        Probability of regrowth from burned to forest per step,
        by default 0.1.
    """
    self.prob_combustion = prob_combustion
    self.prob_regrowth = prob_regrowth
    self.create_neighborhood(strategy=Queen, use_index=True)