Wildfires are intensifying with climate change and human activity. This thesis develops a modular agent‑based simulation in Python (Mesa) to compare three aerial suppression strategies: autonomous drone swarms, conventional aircraft, and hybrid deployments. Agents represent fires, drones, aircraft, and resource stations with parameters grounded in current literature and manufacturer specifications. Drone path‑planning is driven by A* search, Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC), enabling analysis of coordination, response behavior, and resource management.
Abstract
Research Questions
- How do drone swarms, conventional aircraft, and hybrid strategies differ in response time and coverage?
- What trade‑offs emerge across water consumption, energy use, emissions, and cost?
- How do A*, ACO, and ABC influence path selection, coordination, and suppression outcomes?
Methods
Agent‑Based Framework
Mesa‑based grid world with agents for fires, drones, aircraft, and resource stations. Parameters and dynamics align with current literature and manufacturer specifications.
Path Planning
Drones use A* for heuristic shortest‑path baselines; ACO for pheromone‑guided coordination; and ABC for exploration‑exploitation of candidate routes.
Metrics & Analysis
Response latency, area coverage, containment progression, water/energy use, emissions, and operational cost are collected throughout runs.
Key Findings
Hybrid Effectiveness
Hybrid deployments leverage drones for rapid localization and aircraft for bulk suppression, improving overall containment dynamics.
Swarm Coordination
Nature‑inspired planners (ACO/ABC) support emergent task allocation and reduce redundant coverage compared to purely heuristic routing.
Resource Trade‑offs
Drones reduce human risk and fuel use but are constrained by water capacity and energy cycles; refilling logistics strongly shape outcomes.
Nature‑inspired coordination strategies enable scalable, human‑safe suppression while accounting for sustainability and cost.
Contributions
- Modular, open‑source ABM for aerial wildfire suppression (Mesa) with documented agents and metrics.
- Comparative analysis of A*, ACO, and ABC for aerial path planning in dynamic fire environments.
- Evaluation of hybrid tactics that balance response speed, sustainability, and cost.