SwarmFusion: Revolutionizing Disaster Response with Swarm Intelligence and Deep Learning

TL;DR

SwarmFusion combines swarm intelligence and deep learning to improve real-time disaster response by optimizing resource allocation and path planning, significantly enhancing speed and coverage in simulated flood and wildfire scenarios.

Contribution

It introduces a hybrid framework integrating particle swarm optimization with CNNs for disaster response, a novel approach for real-time situational awareness and operational efficiency.

Findings

Up to 40% faster response times in simulations

Achieved 90% survivor coverage in disaster scenarios

Demonstrated scalability across diverse crisis types

Abstract

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.