Agent-based modeling of autonomous aerial wildfire suppression
A comparative study of drone swarms, traditional aircraft, and hybrid systems
Visualization of A* pathfinding algorithm showing agent movement patterns in wildfire suppression simulation
// Abstract
Wildfires pose a growing threat resulting from climate change, bio- diversity loss, and human activity. This research explores economic and sustainable firefighting approaches using autonomous drone swarms, traditional aircraft, and hybrid models through agent-based modeling. A simulation framework created in Python using the Mesa library evaluates these methods, focusing on cost, sustainability (water, emis- sions), and computational efficiency. Drones, planes, fires, and resource stations were modeled as agents with parameters grounded in current research. Three path-finding algorithms (A*, Ant Colony Optimiza- tion, and Artificial Bee Colony) were implemented to guide drone behavior. The framework incorporates machine learning-based risk assessment to identify high-risk fire zones. This research contributes an open-source, modular simulation environment for evaluating aerial firefighting strategies, providing evidence-based insights for sustainable and efficient wildfire management practices.
// Key Findings
Autonomous drone swarms demonstrated 42% faster initial response compared to traditional aircraft
Improved area coverage efficiency through coordinated swarm behavior and optimal path planning
Significant operational cost savings through reduced fuel consumption and pilot requirements
Dramatic reduction in human risk exposure while maintaining suppression effectiveness
// Methodology
Interactive Agent Types
Click on each agent type to learn more about their role in the simulation:
Fire
Dynamic fire cells
Drone
Autonomous units
Aircraft
Traditional planes
Recharge Station
Energy refill
Water Station
Water refill
// Interactive Mesa-Based Simulation
This is a toned down version of the original model developed in the thesis to play around with.
Future Work
- Integration of machine learning algorithms for predictive fire behavior modeling
- Real-time weather data incorporation for dynamic simulation updates
- Extended multi-agent communication protocols for improved coordination
- Physical drone prototyping and field testing validation
- Economic impact analysis and policy recommendation framework