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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63494
Volume:
10 |
5 |
May 2026
Pages:
1-9
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
department of management studies
vivekananda global university
department of computer science
vivekananda global university
department of paramedical sciences
vivekananda global university
Artificial intelligence has emerged as a game changer in disaster management, enabling rapid prediction, real-time monitoring and coordinated emergency response. Despite impressive technological advances that have resulted in prediction accuracies of over 90%, there are still large gaps between current capabilities and the requirements of effective, equitable and sustainable deployment of disaster management systems globally. This study conducts a systematic literature review and gap analysis of over 45 peer-reviewed papers to identify 12 critical and addressable gaps in AI-powered disaster management systems. Three gaps management for rapid prediction, real-time monitoring and coordinated emergency response. But despite huge technological advances We’re seeing prediction accuracies above 90%, but there are still huge gaps between where we are and where we need to be to effectively, equitably and sustainably deploy disaster management systems worldwide. We perform a systematic literature review and gap analysis of 45+ peer-reviewed papers to reveal 12 critical, solvable gaps in AI-powered disaster management systems. Three holes become particularly crucial: (1) Transparency and Explainability Crisis, where 68% of systems lack adequate documentation and 42% do not offer explanations for their recommendations; (2) Data Quality and Low-Resource Deployment Obstacles, hindering deployment in disaster-prone yet economically challenged areas; and (3) System Integration and Inter-Agency Coordination Failures, leading to fragmented and reactive responses. For each gap, we propose evidence-based solutions such as explainable AI frameworks, transfer learning methodologies, federated learning architectures, edge computing implementations, and standard data integration protocols. This research indicates that bridging these gaps requires not only technological innovation, but also policy alignment, stakeholder engagement, and human-centered design principles. The results offer a strategic roadmap for researchers, policymakers and practitioners to advance the deployment of AI in disaster management, toward more effective, equitable and resilient emergency response systems globally.
KEYWORDS
The convergence of advances in AI, proliferation of IoT, and increasing frequency of disasters presents an unprecedented opportunity to shift the paradigm of global disaster management from reactive crisis response to proactive, coordinated, equitable protection. Realizing this opportunity requires not only better algorithms, but systematic attention to transparency, equity, integration, human-centered design, and governance. By targeting the 12 critical gaps identified in this research, beginning with the three interventions that are highest impact and most feasible, we can move toward disaster management systems that truly serve all populations and minimize human suffering in the face of natural hazards. There is the technology. We know the answers. The rest is the commitment of everyone to put it into effect.
[1] Yadav, H. (2025). Early Natural Disaster Prediction Using Machine Learning: A Comprehensive Review. International Journal of Scientific Research and Engineering Management, 44(4), 16. DOI: 10.55041/ijsrem44416 [2] Bajwa, A. (2025). AI-Based Emergency Response Systems: A Systematic Literature Review on Smart Infrastructure Safety. DOI: 10.63125/xcxwpv34 [3] Mohmedmhdi, H., Sreelatha, D. T., Bhaskaran, B., Abdullayev, D., Nithya, V. P., & Rahman, D. (2025). Real-Time Disaster Prediction with Mobile Sensor Networks. Journal of Open World Ubiquitous and Pervasive Intelligence, 2(049). DOI: 10.58346/jowua.2025.i2.049 [4] Hasan, M. M., & Pinky, K. N. (2024). Advanced Computing and AI-Driven National Information Systems for Predictive Disaster Risk Management and Economic Loss Mitigation. DOI: 10.63125/4sbz5j45 [5] Visave, J. (2025). Transparency in AI for emergency management: building trust and accountability. AI and Ethics, 5(2), 692. DOI: 10.1007/s43681-025-00692-x [6] Pahuriray, A. V., & Cerna, P. D. (2025). IoT-Enabled Flood Monitoring and Early Warning Systems: A Systematic Review. International Journal of Computer Science and Mobile Computing, 14(4), 5. DOI: 10.47760/ijcsmc.2025.v14i04.005 [7] Abid, S. K., Roosli, R., Nazir, U., & Kamarudin, N. S. (2025). AI-enhanced crowdsourcing for disaster management: strengthening community resilience through social media. International Journal of Emergency Medicine, 12245, 1009. DOI: 10.1186/s12245-025-01009-9
Publication Date
6/2/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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