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Wildfire Autonomous Response and Prediction Using Cellular Automata (WARP-CA)

Abdelrahman Ramadan

TL;DR

Wildfire modeling must adapt to rapid, wind-influenced dynamics and terrain heterogeneity. The paper presents WARP-CA, a framework that combines Perlin-noise terrain generation with cellular automata for fire spread and leverages both SARL and MARL (via PPO, A2C, DQN) to train autonomous agents (UAVs/UGVs) for suppression and forest preservation. It introduces a CTDE-compatible MARL pipeline, an integrated reward design, and a FireExtinguishingEnvParallel interface within PettingZoo for multi-agent collaboration, showing that PPO-based policies can achieve cooperative strategies while highlighting computational and wind-model simplifications as avenues for future improvement. The approach advances wildfire management by enabling emergent, coordinated agent behaviors in a physically inspired simulation, with practical implications for real-time suppression and terrain-aware planning.

Abstract

Wildfires pose a severe challenge to ecosystems and human settlements, exacerbated by climate change and environmental factors. Traditional wildfire modeling, while useful, often fails to adapt to the rapid dynamics of such events. This report introduces the (Wildfire Autonomous Response and Prediction Using Cellular Automata) WARP-CA model, a novel approach that integrates terrain generation using Perlin noise with the dynamism of Cellular Automata (CA) to simulate wildfire spread. We explore the potential of Multi-Agent Reinforcement Learning (MARL) to manage wildfires by simulating autonomous agents, such as UAVs and UGVs, within a collaborative framework. Our methodology combines world simulation techniques and investigates emergent behaviors in MARL, focusing on efficient wildfire suppression and considering critical environmental factors like wind patterns and terrain features.

Wildfire Autonomous Response and Prediction Using Cellular Automata (WARP-CA)

TL;DR

Wildfire modeling must adapt to rapid, wind-influenced dynamics and terrain heterogeneity. The paper presents WARP-CA, a framework that combines Perlin-noise terrain generation with cellular automata for fire spread and leverages both SARL and MARL (via PPO, A2C, DQN) to train autonomous agents (UAVs/UGVs) for suppression and forest preservation. It introduces a CTDE-compatible MARL pipeline, an integrated reward design, and a FireExtinguishingEnvParallel interface within PettingZoo for multi-agent collaboration, showing that PPO-based policies can achieve cooperative strategies while highlighting computational and wind-model simplifications as avenues for future improvement. The approach advances wildfire management by enabling emergent, coordinated agent behaviors in a physically inspired simulation, with practical implications for real-time suppression and terrain-aware planning.

Abstract

Wildfires pose a severe challenge to ecosystems and human settlements, exacerbated by climate change and environmental factors. Traditional wildfire modeling, while useful, often fails to adapt to the rapid dynamics of such events. This report introduces the (Wildfire Autonomous Response and Prediction Using Cellular Automata) WARP-CA model, a novel approach that integrates terrain generation using Perlin noise with the dynamism of Cellular Automata (CA) to simulate wildfire spread. We explore the potential of Multi-Agent Reinforcement Learning (MARL) to manage wildfires by simulating autonomous agents, such as UAVs and UGVs, within a collaborative framework. Our methodology combines world simulation techniques and investigates emergent behaviors in MARL, focusing on efficient wildfire suppression and considering critical environmental factors like wind patterns and terrain features.
Paper Structure (59 sections, 15 equations, 26 figures, 1 table, 1 algorithm)

This paper contains 59 sections, 15 equations, 26 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. Existing Techniques and Their Limitations
  4. Need for a Novel Approach
  5. Contributions
  6. Literature Review
  7. Comprehensive Review on Wildfire Simulation: Models, Algorithms, and Terrain Generation
  8. Autonomous Wildfire Management and Response Systems: A MARL Perspective
  9. Methodology
  10. World Simulation
  11. Terrain Generation with Perlin Noise
  12. Mathematical Definition of Perlin Noise
  13. Gradient Vector Generation
  14. Noise Calculation
  15. Smoothing Function
  16. ...and 44 more sections

Figures (26)

  • Figure 1: Smoothed Perlin Noise Map
  • Figure 2: The Effect of Choosing Different Scales on Perlin Noise Generation.
  • Figure 3: Progression of Fire Spread using CA
  • Figure 4: Progression of the Forest Fire simulation with additional Environmental Complexities
  • Figure 5: Pipeline from World Simulation to SARL and MARL Training and Deployment.
  • ...and 21 more figures