Quantum-Enhanced Disaster Assessment and Management (QuanDAM) System – A Perspective

Authors

Mohammed Sahil Nakhuda, Manipal Institute of Technology, MAHE ManipalFollow
Shweta Vincent Dr., Manipal Institute of Technology, MAHE ManipalFollow
Om Prakash Kumar Dr., Manipal Institute of Technology, MAHE ManipalFollow

Abstract

Efficient disaster response hinges on the rapid identification of damaged structures post-natural disasters. This literature review surveys diverse solutions, emphasizing merits, drawbacks, and performance metrics. Techniques such as deep learning with pre-trained models, transfer learning with CNNs, and incremental learning with SVMs are scrutinized for their computational demands and adaptability. Ensemble learning, CNNs, attention-based models, transformer networks, and hybrid approaches offer distinct advantages like heightened accuracy and resource efficiency. Challenges, including computational complexity and cost, accompany these methods. Additionally, we propose a framework termed Quantum-Enhanced Disaster Assessment and Management (QuanDAM) which encompasses the usage of Artificial Intelligence (AI) predictive modelling, health monitoring of structures, material integration, and usage of microbots for disaster management and recovery.

Recommended Citation

Nakhuda, Mohammed Sahil; Vincent, Shweta Dr.; and Kumar, Om Prakash Dr. (2023) "Quantum-Enhanced Disaster Assessment and Management (QuanDAM) System – A Perspective," Manipal Journal of Science and Technology: Vol. 8: Iss. 2, Article 1.
Available at: https://impressions.manipal.edu/mjst/vol8/iss2/1