SwarmFusion: Revolutionizing Disaster Response with Swarm Intelligence and Deep Learning
This provides a transformative solution for time-critical disaster management, benefiting emergency responders in chaotic environments like floods and wildfires.
The paper tackled the problem of rapid, adaptive decision-making in disaster response by developing SwarmFusion, a hybrid framework that integrates particle swarm optimization with convolutional neural networks, resulting in up to 40% faster response times and 90% survivor coverage in simulations.
Disaster response requires rapid, adaptive decision-making in chaotic environments. SwarmFusion, a novel hybrid framework, integrates particle swarm optimization with convolutional neural networks to optimize real-time resource allocation and path planning. By processing live satellite, drone, and sensor data, SwarmFusion enhances situational awareness and operational efficiency in flood and wildfire scenarios. Simulations using the DisasterSim2025 dataset demonstrate up to 40 percentage faster response times and 90 percentage survivor coverage compared to baseline methods. This scalable, data-driven approach offers a transformative solution for time-critical disaster management, with potential applications across diverse crisis scenarios.