Multimodal Intelligence Framework for Early Detection of Socio-Political Instability and Autonomous Optimization of Humanitarian Aid Distribution in Conflict Zones

• Abbas Ahsun

  Texas University

  Author

The intersection of armed conflict, food insecurity, and humanitarian logistics presents a critical challenge where early detection of socio-political instability and efficient aid distribution remain largely disconnected domains. Current early warning systems rely predominantly on unimodal data streams, while humanitarian logistics optimization operates reactively rather than predictively. This study develops and validates a multimodal intelligence framework that integrates satellite imagery, conflict event data, news analytics, and social media signals for instability prediction, coupled with autonomous optimization of aid distribution networks. Using a design-based research methodology combining retrospective analysis of conflict data (2020–2025) and prospective simulation, the framework achieved 89.4% prediction accuracy for instability onset within a 30-day horizon, outperforming baseline unimodal models by 14.2 percentage points. The autonomous distribution component reduced simulated operational costs by 18.7% and unmet demand by 23.1% compared to static allocation methods. Feature importance analysis identified conflict recurrence patterns, food price anomalies, and checkpoint congestion as the most predictive indicators. The framework offers a replicable architecture for anticipatory humanitarian action, enabling practitioners to shift from reactive response to predictive intervention. These findings contribute to both computational conflict research and humanitarian operations literature by demonstrating the viability of integrated prediction-optimization systems in conflict-affected environments.

• pdf

Copyright (c) 2026 Abbas Ahsun (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.