2025 Volume 9
Systematic Literature Review of Graph Neural Networks in Disaster Management: Methods, Applications, and Future Directions
Author
Sularno1*, Wendi Boy2, Putri Anggraini3, Rometdo Muzawi4, Renita Astri5 *
Abstract: Abstract: The increasing frequency and complexity of natural disasters have heightened the urgency for more intelligent, adaptive, and data-driven disaster management systems. This study presents a Systematic Literature Review (SLR) on the application of Graph Neural Networks (GNNs) in disaster management, aiming to provide a comprehensive synthesis of current methodologies, applications, and research gaps. Employing the PRISMA 2020 framework and PICOC formulation, a total of 4,087 studies from IEEE, Scopus, SpringerLink, and ACM Digital Library were screened, resulting in 50 relevant articles published between 2019 and 2025. The review reveals that GNNs, particularly Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and hybrid models like GNN-GRU and GNN Transformer, have been widely implemented across various disaster phases including mitigation, emergency response, and post-disaster recovery. GNNs are primarily applied in flood and wildfire prediction, evacuation planning, infrastructure damage assessment, and pandemic modeling. Their ability to model spatiotemporal relationships makes them effective tools for handling complex disaster-related data. However, key challenges persist, including issues of scalability, data quality, model interpretability, and limited cross-disaster generalizability. To address these, future research should explore more interpretable and scalable architectures, improved integration with IoT systems, and the development of general-purpose GNN models for multi-disaster scenarios. This review contributes valuable insights for researchers, policymakers, and system developers aiming to build transparent, responsive, and robust AI-based disaster management frameworks.
2025 Volume 9
Legal Protection for Unregistered Workers in Social Security: Case Study of Domestic Workers
Ikhwan Aulia Fatahillah, Moh Mahbub, Neng Yani Nurhayani, Opik Rozikin
Abstract: The Employment Social Security Program is designed to provide protection for workers by offering benefits to replace income lost due to events such as work-related accidents, illness, and old age. This study examines the legal protection for workers not registered in the Employment Social Security Program in Banjar City, West Java Province, with a specific focus on domestic helpers. Out of approximately 1,000 domestic workers in the city, only around 400 are registered in the Employment Social Security Program. The study employs a descriptive-analytical method, utilizing qualitative data from primary sources such as lists of vulnerable workers and secondary data from relevant literature. Findings reveal that legal protection for workers is regulated under Banjar City Regional Regulation No. 9 of 2023 on Optimizing Employment Protection through Employment Social Security. This regulation covers key aspects such as protection, facilitation for vulnerable workers, and collaboration with relevant parties. Major barriers to Employment Social Security registration include a lack of understanding of the registration process and the perception that registration costs are too high. To improve registration rates, local government needs to enhance outreach efforts, improve the quality of law enforcement, and raise public legal awareness. These measures are expected to create fairer protection for all workers in Banjar City.
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