Integrating ABM and GIS for Flood Evacuation Planning: A Systematic Review and Future Direction

This systematic review examines the integration of agent-based modeling (ABM) and Geographic Information Systems (GIS) in flood evacuation planning from 2015 through early 2025. This review aims to systematically evaluate how ABM and GIS have been integrated in flood evacuation research, identify methodological gaps, and propose a structured framework to guide future model development. Using PRISMA guidelines, 67 studies were selected and analyzed to uncover methodological trends, empirical gaps, and policy relevance in this growing research domain. Using the PRISMA 2020 framework, the analysis reveals a dominant reliance on mesoscopic modeling (43%), limited real-time data integration (17.9%), weak empirical validation practices (16.4%), and minimal machine learning adoption (4.5%). To structure the evolving landscape, a conceptual integration framework is proposed to classify studies by modeling scale, data fidelity, and validation strategy. This framework highlights a gradual shift toward behaviorally realistic, spatially precise, and policy-relevant evacuation models. Persistent challenges include limited validation practices, weak real-time responsiveness, and insufficient policy integration. Conclusions were drawn by identifying five research priorities: AI integration, real-time enhancement, multi-hazard modeling, empirical grounding, and participatory policy co-design. This review offers actionable insights for advancing robust, scalable, and operational ABM-GIS systems in disaster risk reduction.

@article{Ibrahim2026,
  title     = {Integrating ABM and GIS for Flood Evacuation Planning: A Systematic Review and Future Direction},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {2},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Kabir Musa Ibrahim and Abubakar Ahmad and Noor Akma Abu Bakar and Mazlina Abdul Majid and Azamuddin Rahman},
  doi       = {10.14569/IJACSA.2026.0170221},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170221}
}