2022· International Journal of Multidisciplinary Research and Growth Evaluation· Vol 3, pp. 1108-1121· 0 citations
Abstract
Humanitarian and disaster response supply chains operate under extreme uncertainty, time pressure, and resource constraints, where delays or misallocations directly translate into human suffering and loss of life. In recent years, predictive intelligence models have emerged as critical enablers for enhancing supply chain resilience by improving anticipatory decision-making, situational awareness, and adaptive coordination across complex humanitarian networks. This review examines conceptual advances in predictive intelligence models applied to humanitarian and disaster response supply chains, with emphasis on their theoretical foundations, methodological evolution, and resilience-oriented capabilities. The paper synthesizes developments across data-driven forecasting, probabilistic risk modeling, machine learning, and hybrid human–AI decision frameworks, highlighting how these approaches support demand anticipation, disruption prediction, inventory pre-positioning, and logistics network reconfiguration. Particular attention is given to the integration of real-time data streams from remote sensing, social media, Internet of Things devices, and institutional reporting systems, as well as the role of explainability and trust in high-stakes humanitarian contexts. The review also discusses persistent challenges, including data sparsity, ethical constraints, model transferability across disaster types and regions, and governance issues related to inter-agency coordination. By organizing the literature around resilience dimensions—robustness, adaptability, and recoverability—the paper offers a unifying conceptual lens for evaluating predictive intelligence models beyond pure accuracy metrics. The study concludes by identifying research gaps and proposing future directions, including human-centered predictive systems, federated and privacy-preserving learning, and policy-aligned intelligence architectures. Overall, the review provides a structured foundation for researchers, practitioners, and policymakers seeking to leverage predictive intelligence to strengthen humanitarian supply chain resilience in increasingly volatile disaster environments.
This paper develops an integrative conceptual framework that links AI-enabled compliance assurance with resilient coordination across national emergency supply networks and contributes to policy design, system architecture development, and future research on resilient, compliant, and adaptive emergency supply chain ecosystems.
Abiola Idowu, Esther Sydney, Glory Ohunyon et al.· International Journal of Mul...· 0 citations
Modern Disaster Management Systems (DMS) are currently shifting from reactive hazard response to proactive, intelligent resilience architectures. As global hazards escalate in both frequency and severity, classical mitigation lifecycles are increasingly burdened by severe latency in information processing and resource allocation.
To address these shortcomings, a comprehensive, Artificial Intelligence-driven framework for disaster management is proposed, which synthesizes state-of-the-art computational models with foundational hazard theories. By integrating Geographic Information Systems (GIS), Internet of Things (IoT) distributed sensor networks, and sophisticated multimodal data fusion techniques, this proposed system accurately processes disparate spatial and environmental data streams in real time. Crucially, the architecture utilizes the Gemini 2.5 multimodal model to orchestrate extreme-scale context management and to execute next-generation agentic capabilities across decentralized emergency response networks.
Furthermore, the paper critically examines the socio-technical challenges associated with crisis management AI and proposes explicit methodologies to mitigate spatial blind spots in vulnerability mapping as well as algorithmic bias. The ultimate result is a translational, highly scalable AI framework engineered for equitable, real-world deployment.
Risav Dey· International Journal For Mu...· 0 citations
Artificial intelligence (AI) stands at the forefront of transforming emergency management, offering unprecedented capabilities in disaster preparedness and response. Recent implementations demonstrate this shift from reactive to proactive approaches, particularly through flood prediction algorithms and maritime search-and-rescue optimization systems that integrate real-time vessel locations and weather data. However, the current landscape reveals a critical challenge: The opacity of AI systems creates a significant trust deficit among emergency responders and communities. Research findings paint a concerning picture of this transparency gap. A comprehensive survey of emergency management AI systems reveals striking statistics: 68 percent lack adequate documentation of their data sources, while 42 percent fail to provide clear justifications for their recommendations. This “black box” phenomenon carries serious implications, particularly when flood prediction models disproportionately affect vulnerable populations or when opaque decision-making processes lead to suboptimal resource allocation during critical rescue operations. Analysis of real-world applications in flood preparedness and search-and-rescue operations exposes systematic communication deficiencies within these essential emergency response frameworks. The research examines how varying levels of AI transparency directly influence emergency responders’ decision-making during crises, exploring the delicate balance between operational openness and security considerations. These findings highlight an urgent need for robust oversight mechanisms and context-specific transparency protocols to ensure ethical AI deployment in emergency management. The evidence points toward a clear solution: developing human-centric approaches that enhance rather than replace human capabilities in emergency response. This strategy requires establishing tailored transparency guidelines and monitoring systems that address current challenges while facilitating effective AI integration. By prioritizing both technological advancement and human oversight, emergency management systems can better serve their critical public safety mission.
Jaideep Visave· Journal of Emergency Managem...· 0 citations
Natural disasters cause significant human, economic, and infrastructural losses worldwide, yet traditional disaster response systems often rely on static risk assessments and isolated data sources that fail to capture evolving severity, spatial dependencies, and real-time operational constraints such as infrastructure capacity and transportation logistics. To address these challenges, this work proposes an integrated multi-modal disaster response framework that combines satellite imagery analysis, social media–derived situational intelligence, graph neural network (GNN)–based spatial risk prediction, and reinforcement learning–driven resource allocation. Disaster severity is first estimated through multimodal fusion of remote sensing damage indicators and social media urgency signals. A GNN then models spatial dependencies across disaster zones to generate refined risk scores. Based on these risk estimates, a structured disaster resource database is constructed using population grounding derived from WorldPop 1 km spatial grid density alongside a simulated infrastructure and transport resource database. Resource allocation is formulated as a sequential decision-making problem and optimized using a Proximal Policy Optimization (PPO) reinforcement learning agent, with Deep Q-Network (DQN), greedy heuristic, Mixed Integer Linear Programming (MILP), A* search, and Priority Queue dispatching approaches serving as comparative baselines. Experimental evaluation demonstrates that the PPO-based allocation strategy achieves strong humanitarian impact, with an overall population rescue rate of 92%, including 92.0% of injured individuals and 91.9% of displaced populations successfully assisted across simulated disaster zones, outperforming the DQN baseline by 2.9 percentage points and the greedy heuristic by 5.5 percentage points. These results indicate improved resource utilization efficiency and adaptive allocation compared to baseline methods while suggesting potential operational feasibility under simulated infrastructure and transport constraints. Visualization-driven analysis further enhances interpretability and supports transparent decision-making. Future work will emphasize the integration of dynamic infrastructure information, including sources from OpenStreetMap (OSM), and extend the framework toward operational decision-support deployment in large-scale disaster response scenarios.
S. B. Priya, S. K. Sanju, S. Priyanga et al.· IEEE Access· 0 citations
Global supply networks face compound disruptions arising from geopolitical conflict, climate hazards, cyber incidents, logistics bottlenecks, demand volatility, and policy uncertainty. Existing studies demonstrate that big data analytics can improve forecasting and supply chain performance, but they seldom explain the full conversion mechanism through which data resources become actionable resilience or how firm-level resilience contributes to trade continuity and economic performance. This article develops a multilevel capability to outcome framework that links six stages: multi-source data acquisition, governed data integration, predictive intelligence, resilience orchestration, international trade continuity, and productivity-oriented growth outcomes. The theoretical novelty lies in three mechanisms. First, it distinguishes predictive accuracy from decision actionability and identifies resilience orchestration as the dynamic capability that converts forecasts into coordinated operational responses. Second, it specifies cross-level transmission from firm capability to supply-network stability and then to trade continuity. Third, it defines explicit boundary conditions, including shock observability, response discretion, input substitutability, network concentration, cyber exposure, and institutional digital capacity. To address feasibility concerns, the paper proposes a focused first-stage validation in export-oriented automotive-component manufacturing using a three-wave, 24-month panel that combines survey measures with operational and shipment records. Longitudinal structural equation modelling and firm fixed-effects estimation are designated as the primary methods; machine-learning comparison and macroeconomic aggregation are retained as secondary extensions rather than simultaneous requirements. Economic growth is treated as a distal outcome mediated by resilient trade and productivity, not as a direct consequence of technology adoption. The revised framework offers a more parsimonious, falsifiable, and practically implementable research program for firms and policy institutions.
Md Alamgir Miah· Journal of Intelligent Decis...· 0 citations
Climate change, biodiversity loss, resource depletion, and increasingly volatile environmental conditions require governance systems that can move beyond retrospective monitoring toward anticipatory and adaptive action. Artificial intelligence (AI) offers relevant capabilities, but current environmental applications remain fragmented across sensing, prediction, optimization, and decision support. This study develops the AI Enabled SocioEcological Resilience Framework (AI SERF) through a systematic literature review and qualitative conceptual synthesis of peer reviewed studies published between January 2022 and June 2026. The reported review process screened 412 records and retained 73 studies for thematic synthesis. The revised framework links four functional pillars, namely Autonomous Eco Monitoring, Predictive Resource Optimization, Adaptive Algorithmic Governance, and Eco Resilient Feedback Loops, to absorptive, adaptive, and transformative resilience capacities. Its novelty lies not in proposing another isolated AI architecture, but in connecting data acquisition, predictive intelligence, human supervised governance, ecological intervention, and learning within a single resilienceoriented cycle. The framework is operationalized through candidate data sources, AI models, governance actors, performance indicators, and responsible AI safeguards. Particular attention is given to explainability, energy and carbon efficiency, algorithmic bias, cyberphysical security, institutional capacity, and data limitations in tropical and archipelagic settings. The study provides a theoretically grounded and implementation oriented basis for future empirical validation of AI enabled environmental governance and clarifies its contribution to SDGs 9, 11, 13, 14, and 15
Ayu Rimanda, Prima Wira Nanda, Zakia Hary Nisa et al.· Advanced Robotics· 0 citations
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