This systematic review aims to synthesise Web-GIS evidence for managing natural hazards to share state-of-the-art practices and policies. A comprehensive search in SCOPUS, among other databases, identified 1775 articles published between 2014 and 2023. Following a selection process based on the PRISMA model, 65 articles met the inclusion criteria. The analysis revealed a growing trend over the past decade, with most research concentrated in the last three years. Eight crucial subtopics within the Web-GIS domain have emerged: Integrated Spatial Analysis and Modelling, Technologies and Infrastructure, Visualisation and User Interface Design, Decision Support Systems, Real-time Monitoring and Early Warning, Disaster Recovery and Resilience, Citizen and Social Media Integration, and Multi-Stakeholder Collaboration. A substantial contribution of the literature has been identified in Decision Support Systems and Integrated Spatial Analysis, reflecting their vital role in strategising and predicting hazard impacts. Furthermore, a geographical distribution analysis revealed significant Web-GIS applications in countries like Italy and China, alongside a deficit in low- and middle-income countries. It also highlights potential gaps in hazard studies, including the need to prioritise heatwave management in the face of climate change. This research calls for policymakers and practitioners to leverage evidence-informed decision making and foster community collaboration for enhanced natural disaster resilience.

In an era of increasingly abundant and granular spatial and temporal data, the traditional divide between environmental GIS and building-centric BIM scales is diminishing, offering an opportunity to enhance natural hazard assessment by bridging the gap between territorial impacts and the effects on individual structures. This study addresses the challenge of integrating disparate data formats by establishing a centralised database as the foundation for a comprehensive risk assessment approach. A use case focusing on flood risk assessment for a public building in northwest Italy demonstrates the practical implications of this integrated methodology. The proposed TErritorial RIsk Management & Analysis Across Scale (TERIMAAS) framework utilises this centralised repository to store, process, and dynamically update diverse BIM and GIS datasets, incorporating real-time IoT-derived information. The GIS spatial analysis assesses risk scores for each hazard type, providing insights into vulnerability and potential impacts. BIM data further refine this assessment by incorporating building and functional characteristics, enabling a comprehensive evaluation of resilience and risk mitigation strategies tailored to dynamic environmental conditions across scales. The results of the proposed scalable approach could provide a valuable understanding of the territory for policymakers, urban planners, and any stakeholder involved in disaster risk management and infrastructure resilience planning.

The growing complexity and interdependence of critical infrastructures (CIs), increasingly exposed to natural and technological hazards, call for educational approaches to enhance resilience and risk management. This study examines trends, patterns, and challenges in integrating digital and immersive technologies into education and training for stakeholders in critical infrastructure management. A systematic review of peer-reviewed literature was conducted using Scopus as the primary source, covering the last decade and analyzing the corpus across six dimensions: technological approach, pedagogical model, hazard typology, infrastructure domain, stakeholder category, and implementation phase. Following the PRISMA framework, 5635 records were identified and screened through a multistage process combining rule-based filtering and manual review, resulting in 105 papers meeting the inclusion criteria. The analysis reveals a shift from classroom instruction and physical drills toward immersive, simulation-based, and data-informed learning ecosystems that strengthen situational awareness, procedural accuracy, and decision-making under stress. However, the review identifies persistent gaps in evaluation metrics, cross-sector frameworks, and collaborative learning environments that limit adoption. The findings underscore that digital and immersive technologies can reconfigure education and training frameworks, enabling the formation of Resilient Operators endowed with adaptive cognition, continuous learning capacities, and responsiveness to natural hazard-induced technological risks.

Virtual reality is increasingly used for procedural and safety training in complex technical facilities, but most VR training systems remain constrained by predefined scenarios, scripted interactions and fixed learning paths. This limits their ability to answer unexpected questions, address role-specific needs and context-dependent procedural errors. This paper asks how large language models can be integrated into VR-based training for critical infrastructure to support adaptive, role-aware and knowledge-grounded interaction. It proposes ATHENA, a knowledge-grounded two-loop framework in which a reactive loop answers learner-initiated questions and a proactive loop supports system-initiated diagnosis, guidance and safety intervention. The framework links VR scene state, role affordances, authorised knowledge sources, safety rules and provenance records to an LLM-mediated tutor. Learner competence is represented with the Procedural Knowledge Component model, which decomposes embodied procedural mastery into four axes: declarative, locomotion, manipulation and verification. ATHENA is instantiated in the Safety and Operations Training Hub, a Unity/OpenXR wastewater treatment plant environment with Visitor, Controller and Operator roles. A participant-free simulated-learner evaluation verifies the proactive loop, including learner-state updating, information-gain probe selection, safety-rule enforcement and auditable decision logging. Results show that the implemented mechanisms behave as specified, that information-gain probing improves belief concentration only modestly under favourable simulated conditions, and that the current belief cannot yet reliably localise a learner’s single weakest competence. The contribution is therefore a controlled and auditable architecture for LLM-mediated VR tutoring, together with a clear boundary between verified engineering behaviour and claims that remain for human validation with potential applicability across other procedural domains.

Published rankings of GeoServer, MapServer and QGIS Server usually rest on one hand-tuned configuration per server under closed-loop load. Under a time-stamped protocol, 816 Sobol-sampled sessions per platform ran under open-loop load on exclusive nodes of two processor generations. Surrogates gave sensitivity indices and ranking probabilities. For capacity, the engine's total-order index (0.11) was below those of data complexity, image size, core count and request projection; latency was dominated by data complexity, image size and, for tail latency, the request projection. GeoServer had higher capacity than MapServer in 56% of the space, and capacity rankings reversed in 31% of workload cells on one platform and 35 to 41% for every pair on the other. At half its own capacity, GeoServer usually had the lowest median latency but the greatest relative tail inflation. The FastCGI worker count changed MapServer and QGIS Server capacity about 5.5-fold, and reprojection cost 30 to 69%. Surrogates did not transfer between processor generations. With 32 Bayesian-optimisation proposals each, GeoServer and MapServer tied within measurement resolution at a reference workload and QGIS Server reached 18% less. Rankings depended on configuration within the tested space, so benchmarks should sample configurations, use open-loop load and verify output equivalence.

In an era of increasingly abundant and granular spatial and temporal data, the traditional divide between environmental GIS and building-centric BIM scales is diminishing, offering an opportunity to enhance natural hazard assessment by bridging the gap between territorial impacts and effects on individual structures. This study addresses the challenge of integrating disparate data formats by establishing a centralised database as the foundation for a comprehensive risk assessment approach. A use case focusing on flood risk assessment for a public building in the Piedmont Region demonstrates the practical implications of this integrated methodology. The proposed TErritorial RIsk Management & Analysis Across Scale (TERIMAAS) framework utilises this centralised repository to store, process, and dynamically update diverse BIM and GIS datasets, incorporating real-time IoT-derived information. GIS spatial analysis assesses risk scores for each hazard type, providing insights into vulnerability and potential impacts. BIM data further refines this assessment by incorporating building and functional characteristics, enabling a comprehensive evaluation of resilience and risk mitigation strategies tailored to dynamic environmental conditions across scales. The results of the proposed scalable approach could provide a valuable understanding of the territory for policymakers, urban planners, and any stakeholder involved in disaster risk management and infrastructure resilience planning.