SwarmFusion: Revolutionizing Disaster Response with Swarm Intelligence and Deep Learning
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 21 references that are in the map. First posted on arXiv 10 Jun 2025.
Builds on
4 of 21 references.
References · 4
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Going Deeper with Convolutionscited by 4,241
- 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,162
- 2014Swarm Intelligence Based Algorithms: A Critical Analysiscited by 2
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Citing papers per year: 2026: 1.
1 paper.