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

TL;DR

This paper presents WARP-CA, a novel wildfire modeling approach combining cellular automata with terrain generation and multi-agent reinforcement learning to improve wildfire prediction and autonomous response strategies.

Contribution

Introduces WARP-CA, integrating terrain generation and CA with MARL for wildfire simulation and autonomous suppression, addressing limitations of traditional models.

Findings

Effective wildfire spread simulation using CA and Perlin noise

Potential for autonomous agents to collaborate in wildfire suppression

Insights into emergent behaviors of MARL in wildfire scenarios

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.