Citation

Ahsan, Mohammad Shafat and Alam, Mst Sanjida and Ahmed, Syed Sajjad and Islam, Md Tanzimul and Shoaib, Hashibul Ahsan and Mridha, M. F. and Hossen, Md. Jakir (2026) Attention-Based Geo–Textual Fusion Network for Disaster Risk Prediction. IEEE Open Journal of the Computer Society, 7. pp. 1248-1259. ISSN 2644-1268

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Abstract

Natural disasters pose recurring threats to human life and infrastructure, demanding intelligent systems that can process heterogeneous data streams and provide actionable insights in real time. Existing approaches often treat textual signals from social media and emergency communications separately from spatial hazard attributes, limiting their effectiveness in capturing the full complexity of evolving crises. This paper proposes an AI-driven geo–textual intelligence framework that integrates disaster-related text with GIS-based hazard features for real-time risk prediction and evacuation planning. The framework employs contextual text encoders and a neural GIS encoder, fused through an attention mechanism that dynamically weights cross-modal signals. Experiments on three datasets; social media disaster tweets, multi-label disaster response messages, and flood hazard GIS attributes. It shows that the proposed model achieves an accuracy of 0.94, macro-F1 of 0.89, IoU of 0.81, and kappa of 0.78, surpassing all classical and deep baselines. Evacuation simulations further demonstrate that the model produces safer and faster routes, reducing travel time from 42.1 to 41.5 minutes and increasing the safety index from 0.61 to 0.81. These results highlight the novelty of aligning linguistic cues with spatial context and underscore the potential of deep geo–textual fusion for enhancing situational awareness and supporting critical decision-making in disaster response.

Item Type:	Article
Uncontrolled Keywords:	Deep learning, disaster response
Subjects:	G Geography. Anthropology. Recreation > GB Physical geography (General) > GB3-5030 Physical geography > GB5000-5030 Natural disasters Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 Jun 2026 03:12
Last Modified:	30 Jun 2026 03:12
URII:	http://shdl.mmu.edu.my/id/eprint/16122

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