ForestFire/forest_fire_simulation_complete.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1aa52d82",
   "metadata": {},
   "source": [
    "## 1. Import Libraries\n",
    "\n",
    "Import necessary libraries. NumPy is used for numerical operations, Matplotlib for plotting, and random for generating random numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "47d14b1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd797883",
   "metadata": {},
   "source": [
    "## 2. Constants\n",
    "\n",
    "Define constants used in the simulation. These include the probabilities of fire spread for different vegetation types, the angle of the wind, the ratio of water cells, and the color map for visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "1a896cea",
   "metadata": {},
   "outputs": [],
   "source": [
    "VEGETATION_FIRE_SPREAD_PROBABILITIES = {\n",
    "    'NormalForest': 1.0,\n",
    "    'DryGrass': 1.5,\n",
    "    'DenseTrees': 0.5,\n",
    "    'Water': 0.0\n",
    "}\n",
    "WIND_ANGLE = 30\n",
    "WATER_RATIO = 0.175\n",
    "\n",
    "color_map = {\n",
    "    'NormalForest': 'green',\n",
    "    'DryGrass': 'yellow',\n",
    "    'DenseTrees': 'darkgreen',\n",
    "    'Water': 'blue',\n",
    "    'Burning': 'red',\n",
    "    'Burned': 'black'\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aabedc3",
   "metadata": {},
   "source": [
    "## 3. Initialize Grid\n",
    "\n",
    "Define the function to initialize the grid with a specific size. The grid is initialized with normal forest cells, and specific cells are set to burning, dry grass, and dense trees. Additionally, a certain ratio of water cells is randomly distributed across the grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "120ed3c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_grid(m, n):\n",
    "    grid = [['NormalForest' for _ in range(n)] for _ in range(m)]\n",
    "    grid[m//2][n//2] = 'Burning'\n",
    "    grid[m//4][n//4] = 'DryGrass'\n",
    "    grid[3*m//4][n//4] = 'DenseTrees'\n",
    "    \n",
    "    num_water_cells = int(m * n * WATER_RATIO)\n",
    "    for _ in range(num_water_cells):\n",
    "        i, j = np.random.randint(m), np.random.randint(n)\n",
    "        grid[i][j] = 'Water'\n",
    "    \n",
    "    return grid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cbf8ae7",
   "metadata": {},
   "source": [
    "## 4. Neighbours\n",
    "\n",
    "Define the function to find the neighboring cells of a given cell in the grid. This function is used to determine which cells are adjacent to a burning cell for the fire propagation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "d3bd2e1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def neighbours(grid, i, j):\n",
    "    m, n = len(grid), len(grid[0])\n",
    "    return [(i+a, j+b) for a, b in [(-1,0), (1,0), (0,-1), (0,1), (-1,-1), (-1,1), (1,-1), (1,1)] if 0 <= i+a < m and 0 <= j+b < n]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdb8a0a1",
   "metadata": {},
   "source": [
    "## 5. Calculate Angle\n",
    "\n",
    "Defines the function that calculate the angle between two cells inside the grid to then be used to determine the influence of the wind direction on the fire "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "9e9c1603",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_angle(cell, neighbor):\n",
    "    di = neighbor[0] - cell[0]\n",
    "    dj = neighbor[1] - cell[1]\n",
    "    angle = math.atan2(di, dj) * (180 / math.pi)\n",
    "    return angle % 360"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b76081c5",
   "metadata": {},
   "source": [
    "## 6. Calculate the humidity across the grid\n",
    "\n",
    "Function that calculates the humidity from cells to cells to the nearest water source/cells, used to determine across the grid the levels of humidity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ffcfc62b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def humidity_cross_grid(grid):\n",
    "    m, n = len(grid), len(grid[0])\n",
    "    distance_grid = [[float('inf') for _ in range(n)] for _ in range(m)]\n",
    "    \n",
    "    for i in range(m):\n",
    "        for j in range(n):\n",
    "            if grid[i][j] == 'Water':\n",
    "                distance_grid[i][j] = 0\n",
    "    \n",
    "    for i in range(m):\n",
    "        for j in range(n):\n",
    "            if i > 0:\n",
    "                distance_grid[i][j] = min(distance_grid[i][j], distance_grid[i-1][j] + 1)\n",
    "            if j > 0:\n",
    "                distance_grid[i][j] = min(distance_grid[i][j], distance_grid[i][j-1] + 1)\n",
    "    \n",
    "    for i in range(m-1, -1, -1):\n",
    "        for j in range(n-1, -1, -1):\n",
    "            if i < m - 1:\n",
    "                distance_grid[i][j] = min(distance_grid[i][j], distance_grid[i+1][j] + 1)\n",
    "            if j < n - 1:\n",
    "                distance_grid[i][j] = min(distance_grid[i][j], distance_grid[i][j+1] + 1)\n",
    "                \n",
    "    return distance_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c6a5640",
   "metadata": {},
   "source": [
    "## 7. Calculate the humidity\n",
    "\n",
    "Defines the calculation needed for the function the humidities levels that reduces the probability for a cell to catch fire "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f4f2883e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_humidity(grid):\n",
    "    distance_grid = humidity_cross_grid(grid)\n",
    "    max_distance = max(max(row) for row in distance_grid)\n",
    "    humidity_grid = [[1 - (distance / max_distance) for distance in row] for row in distance_grid]\n",
    "    return humidity_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c44b306",
   "metadata": {},
   "source": [
    "## 8. Calculate the Directional Influence\n",
    "\n",
    "Define the function that returns calculates the influence of the direction of the wind on the fire, based on the wind direction (or angle) between cells "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "917d7507",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_directional_influence(w_direction, angle):\n",
    "    angle_diff = abs(w_direction - angle)\n",
    "    angle_diff = min(angle_diff, 360 - angle_diff)\n",
    "    influence_factor = 1 - (angle_diff / 180)\n",
    "    return influence_factor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e09e60f4",
   "metadata": {},
   "source": [
    "## 9.Propagate with humidity\n",
    "\n",
    "Define the function that updates the state of a single cell based on its neighbors cells, taking into account the humidity, wind and terrain elevation  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "5cf6bfb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def propagate_with_humidity(grid, i, j, p, pstart, w_speed, w_direction, terrain, humidity):\n",
    "    current_state = grid[i][j]\n",
    "    if current_state == 'Burning':\n",
    "        return 'Burned'\n",
    "    if current_state in ['Burned', 'Water']:\n",
    "        return current_state\n",
    "    \n",
    "    neighbors = neighbours(grid, i, j)\n",
    "    for ni, nj in neighbors:\n",
    "        if grid[ni][nj] == 'Burning':\n",
    "            angle = calculate_angle((i, j), (ni, nj))\n",
    "            directional_influence = calculate_directional_influence(w_direction, angle)\n",
    "            \n",
    "            elevation_diff = terrain[ni, nj] - terrain[i, j]\n",
    "            terrain_influence = 1 + elevation_diff / 100\n",
    "            \n",
    "            vegetation_factor = VEGETATION_FIRE_SPREAD_PROBABILITIES.get(current_state, 1.0)\n",
    "            humidity_factor = 1 - humidity\n",
    "            \n",
    "            adjusted_p = p * vegetation_factor * terrain_influence * humidity_factor * (1 + 0.1 * w_speed * directional_influence)\n",
    "\n",
    "            adjusted_p = max(0, min(1, adjusted_p))\n",
    "            \n",
    "            if random.random() < adjusted_p:\n",
    "                return 'Burning'\n",
    "    \n",
    "    if random.random() < pstart:\n",
    "        return 'Burning'\n",
    "    \n",
    "    return current_state\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3156f2fe",
   "metadata": {},
   "source": [
    "## 10. Update Grid\n",
    "\n",
    "Define the function to update the entire grid for one iteration of the simulation, considering all influencing factors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "5780b0cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def update_grid(grid, p, pstart, w_speed, w_direction, terrain, humidity_grid):\n",
    "    m, n = len(grid), len(grid[0])\n",
    "    new_grid = [[None for _ in range(n)] for _ in range(m)]\n",
    "    for i in range(m):\n",
    "        for j in range(n):\n",
    "            new_grid[i][j] = propagate_with_humidity(grid, i, j, p, pstart, w_speed, w_direction, terrain, humidity_grid[i][j])\n",
    "    return new_grid\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7581b1b7",
   "metadata": {},
   "source": [
    "## 11. Convert Grid to RGB\n",
    "\n",
    "Define the function to convert the grid's state to an RGB array for visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "883a6146",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def convert_grid_to_rgb(grid):\n",
    "    m, n = len(grid), len(grid[0])\n",
    "    rgb_array = np.zeros((m, n, 3))\n",
    "    for i in range(m):\n",
    "        for j in range(n):\n",
    "            rgb_array[i, j] = mcolors.to_rgb(color_map[grid[i][j]])\n",
    "    return rgb_array\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72bfa357",
   "metadata": {},
   "source": [
    "## 12. Plot Grid\n",
    "\n",
    "Define the function to plot the grid's state using Matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "f1093819",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def plot_rgb_grid(rgb_array, title=\"Forest Fire Simulation\"):\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.imshow(rgb_array, aspect='auto')\n",
    "    # setting the size of the image to be bigger\n",
    "    fig.set_size_inches(24, 24)\n",
    "    \n",
    "    m, n, _ = rgb_array.shape\n",
    "    ax.set_xticks(range(n))\n",
    "    ax.set_yticks(range(m))\n",
    "    ax.set_xticklabels(range(n))\n",
    "    ax.set_yticklabels(range(m))\n",
    "    ax.set_xlabel(\"X-axis\")\n",
    "    ax.set_ylabel(\"Y-axis\")\n",
    "    \n",
    "    plt.title(title)\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f07d9fe1",
   "metadata": {},
   "source": [
    "## 13. Simulate Function\n",
    "\n",
    "Define the function to orchestrate the forest fire simulation over multiple iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "ef5511f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def simulate(initial_grid, p, pstart, num_iterations, plot_interval, w_speed, w_direction, terrain):\n",
    "    current_grid = initial_grid\n",
    "    humidity_grid = calculate_humidity(initial_grid)\n",
    "    for iteration in range(num_iterations):\n",
    "        current_grid = update_grid(current_grid, p, pstart, w_speed, w_direction, terrain, humidity_grid)\n",
    "        current_grid_rgb = convert_grid_to_rgb(current_grid)\n",
    "        if iteration % plot_interval == 0 or iteration == num_iterations - 1:\n",
    "            print(f\"Iteration: {iteration}\")\n",
    "            plot_rgb_grid(current_grid_rgb)\n",
    "    return current_grid\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4495b0cf",
   "metadata": {},
   "source": [
    "## 14. Main Simulation\n",
    "\n",
    "Define the main part of the script to set up and run the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "93201384",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x2400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 3\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x2400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x2400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 9\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x2400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 12\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x2400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 15\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x2400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 18\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x2400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 21\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x2400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 24\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x2400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 27\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x2400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 29\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x2400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "if __name__ == \"__main__\":\n",
    "    m, n = 128, 128\n",
    "    initial_grid = initialize_grid(m, n)\n",
    "    terrain_elevation = np.random.uniform(low=0, high=100, size=(m, n))\n",
    "    final_grid = simulate(initial_grid, p=0.8, pstart=0.01, num_iterations=30, plot_interval=3, w_speed=0.5, w_direction=WIND_ANGLE, terrain=terrain_elevation)\n",
    "    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}