This research highlights how cohesive teams enhance crisis response through effective communication, high morale, and trust that enable quicker, more effective decision-making during critical situations.
Abstract
Understanding the nature of the event and situation awareness during a natural disaster is crucial for survival and recovery. Management of the disaster event requires seamless coordination across numerous agencies, systems, and stakeholders. A shared understanding of the situation is critical as responders from different organizations (e.g., fire, medical, police, military) must operate under high pressure. This research examines the human-centered aspects of natural disaster management. The intelligent solution, MobiJOPA™ of start-up enterprise Husqtec Corp, served as an intelligent training environment for learning, collecting data, and describing a common event ontology for stakeholders involved with the situation and working together. The study focuses on collecting and describing disaster event information so that all related stakeholders can understand it in the same way. It is important to transform the data into an ontology platform to route it among stakeholders (presentation and sharing of the situational picture and threat assessment) so that main resources can manage the disaster situation? Data for the creation of this ontology platform have been continuously collected from regional training sessions, where participants practiced in virtual disaster-event scenarios.The study highlights the critical role of a common understanding among stakeholders involved in a disaster situation. This research highlights how cohesive teams enhance crisis response through effective communication, high morale, and trust. These factors enable quicker, more effective decision-making during critical situations. Generative AI, machine learning, and autonomous agents can greatly amplify our capabilities but without an ontology platform and semantic backbone analyzing of streams of data in real-time, predicting emerging threats, and optimizing resource distribution, the outputs could be erratic or opaque. With the ontology and knowledge graph in place, AI can reason in context and explain its conclusions using domain concepts that humans understand.
Management of the disaster event requires seamless coordination across numerous agencies, systems, and stakeholders. A shared understanding of the situation is critical as responders from different organizations (e.g., fire, medical, police, military) must operate under high pressure. Satellite data supports situational awareness, and it is crucial to utilize satellite data and ontology platforms in disaster management. This article introduces an analysis of the utilization of ICEYE's SAR satellite technology and semantic ontology platforms in creating a situational picture and situation management of natural disasters, especially major floods. The article highlights that the challenge of traditional optical satellites is their weather dependence, which often makes them unusable in storms and heavy rains. This problem is solved by ICEYE’s SAR radar technology, which is able to penetrate clouds, smoke and darkness, producing weather-independent and real-time geographic information (GIS) from the incident area up to six hours apart. Satellite- based SAR data is important to integrate on regional sensor data (e.g. ground-based sensor fusion data, drone data and weather sensor data).The intelligent solution, MobiJOPA™ of start-up enterprise Husqtec Corp, served as use case environment for situation management. The received satellite data is directed to the semantic infrastructure of the MobiJOPA™ unit, where rule-based artificial intelligence and a common ontology model fuse it with, among other things, terrain elevation models (DEMs) and existing infrastructure information. The system harmonizes data from different sources into an unambiguous situational picture tied to the emergency workers’ own language, which breaks down information silos and effectively prevents misunderstandings between different authorities. The article shows that the seamless interaction of radar data and semantic modeling significantly speeds up critical decision-making.
V. Salminen, Matti Pyykkönen· AHFE International· 0 citations
Disasters triggered by hydro-meteorological, geophysical, and climatic hazards are increasing in frequency and severity, straining the capacity of conventional disaster risk management (DRM) systems that rely on manual data collection and delayed decision-making [1], [5]. Recent advances in Artificial Intelligence (AI), the Internet of Things (IoT), and Geographic Information Systems (GIS) have opened new possibilities for building smart, data-driven, and anticipatory disaster management systems [3], [14]. This paper presents a systematic review of the literature published mainly between 2020 and 2026 that examines how AI, IoT, and GIS are being integrated to support hazard prediction, real-time monitoring, spatial risk assessment, early warning, and post-disaster response. Fifty-seven peer-reviewed and indexed sources were synthesised following a structured review protocol. The review identifies five major integration themes: (i) sensor-driven early warning networks, (ii) geospatial machine-learning hazard susceptibility mapping, (iii) UAV and remote-sensing based damage assessment, (iv) social-media and big-data situational awareness, and (v) digital-twin-enabled smart-city resilience platforms. The paper further develops a conceptual AI-IoT-GIS integration architecture, presents mathematical formulations commonly used for spatial risk indexing, sensor network reliability, and machine-learning performance evaluation, and illustrates these formulations with worked numerical examples, tables, and charts. The review concludes that while integration of these three technologies significantly improves prediction accuracy and response times, challenges remain around data interoperability, energy-constrained sensor networks, algorithmic bias, and the digital divide affecting adoption in low-resource regions [2], [15].
Chirag Patel· Journal of Commerce, Economi...· 0 citations
The increasing frequency and intensity of natural and man-made disasters have highlighted the necessity for intelligent disaster management systems capable of providing rapid response and accurate situational awareness. Conventional disaster management approaches often rely on manual observations, fragmented communication infrastructures, and delayed reporting mechanisms, which can significantly reduce the effectiveness of emergency response operations. The emergence of the Internet of Things (IoT) has introduced new opportunities for real-time monitoring, data acquisition, predictive analytics, and automated decision-making. This paper presents an IoT-enabled disaster management system that integrates distributed sensors, wireless communication networks, cloud computing platforms, and machine learning techniques to improve disaster preparedness, detection, response, and recovery. The proposed framework continuously monitors environmental and structural parameters, analyzes collected information through intelligent algorithms, and generates early warnings for emergency authorities and affected communities. The system aims to minimize casualties, reduce property damage, and enhance coordination among disaster response agencies. Experimental evaluation demonstrates improved prediction accuracy, reduced response time, and enhanced operational efficiency when compared with conventional disaster management systems. The proposed solution offers a scalable, reliable, and cost-effective approach for building resilient smart cities and disaster-resistant communities.
Keywords— Internet of Things, Disaster Management, Smart Cities, Early Warning Systems, Machine Learning, Cloud Computing, Emergency Response.
Kasiraju Rajvardhan Kasiraju Rajvardhan, Islavath Meenakshi Islavath Meenakshi, A. M. A Mamatha· International Journal of Cre...· 0 citations
To manage complex disaster risks, it is important to develop a response that spans multiple areas of expertise. Bridging the gap between law, social sciences, and natural sciences is an important part of any disaster risk reduction. It helps systems adapt quickly as AI technologies are constantly changing and have significantly impacted where law and the natural environment intersect, influencing legal systems and environmental policies, and how legal and environmental issues can prevent AI from having a greater impact on society and the economy. As part of a participatory assessment of production, with the assistance of legal experts, social and environmentalists, the key principles of responsible data analysis are proposed. These principles focus on security, transparency, fairness, accountability, and the ability to challenge or challenge decisions. This conversation describes how different disciplines can work together to create flexible legal systems that use AI, while drawing knowledge from the environmental and social sciences. The way environmentalists and decision-makers talk about useful and accurate information leads to differences that make the use of artificial intelligence difficult due to legal issues related to the reliability, reliability, and contentiousness of disaster management systems. If social media is useful for disaster risk reduction through AI, it's important to consider legal issues related to who is responsible and how sensitive the information used in disaster management is. Ideas for a fair and responsible process focus on the environment and encourage discussions on social and economic issues related to public participation. AI is also very important in education, as it brings together the next generation of law, social and natural sciences to jointly find solutions that bring together different disciplines in a balanced way. AI tools can be very useful in emergency management, but it's important to use them fairly and responsibly. You need to think about things like possible unfair benefits, clear explanations, and protecting people's personal data. Careful use of AI techniques, considering how AI, laws, and environmental risks interact with each other, helps create equitable and sustainable ways to collect and use data.
R. Paper, Research Supervisor Prof, Dominique Ferraro· The social science· 0 citations
Maritime operations entail complex interactions between human operators, vessel systems, and dynamic environmental conditions, rendering shipboard safety management a formidable and persistent challenge. Despite advances in automation and monitoring technologies, severe onboard accidents—particularly those related to confined space entry, work at height, hazardous environments, and human error—continue to occur, while existing safety systems remain largely reactive. This paper presents an ongoing study on an ontology-based collaborative shipboard safety analysis framework that integrates artificial intelligence, Human Digital Twin (HDT) modelling, and digital twin–based visualization to support proactive and explainable safety intelligence. The framework is designed to acquire high-density onboard data through multi-source wearable, environmental, spatial, and operational sensors, and to formalize maritime safety regulations and human–environment interaction knowledge into an ontology-driven knowledge base for context-aware risk inference.A multi-layered system architecture encompassing data acquisition, edge-based processing, HDT modelling, AI-driven risk analysis, digital twin simulation, and feedback-driven learning is introduced. This study establishes a foundational architectural and methodological framework for next-generation shipboard safety intelligence and provides a basis for future experimental validation and real-world deployment.
Hongtae Kim, H. Choi· AHFE International· 0 citations
Sustainable urban disaster response requires Earth observation (EO) resources to be selected according to event demands, environmental conditions, sensing capabilities, and urban targets. This study proposes an Observation Linked Open Data (O-LOD)-based Urban Disaster Observation Task (UDOT) enhancement framework for semantic EO resource configuration. O-LOD organizes heterogeneous data through four dimensions, Event, Context, Subject, and Object, which an instantiation algorithm populates as task graphs. Layered GeoSPARQL queries then match thematic and analytical capabilities, qualify orbit-derived observation opportunities against context and object constraints, and rank feasible alternatives using the Observation Capability Evaluation Model (OCEM). Evaluation on the 2020 Khartoum flood and Bobcat wildfire narrowed 202 satellite–sensor pairs to three flood-capable and four wildfire-capable pairs and returned two Khartoum and eight Bobcat ranked opportunities, with complete queries executing in 3.0 s and 0.07 s. Constraint ablation and resolution sensitivity analyses identified the conditions governing the feasible set. Publicly accessible Sentinel, Landsat, and MODIS products corroborated both retained and excluded results, supporting water-extent and burn-severity mapping where coverage, spectral bands, and image quality met the task requirements. O-LOD therefore provides a traceable semantic link from disaster observation demand to qualified and ranked EO opportunities and subsequent image assessment, supplying task-oriented inputs for downstream scheduling and supporting context-aware sensing for sustainable urban disaster response.
Jie Li, Liang Zhao, Bo Jia et al.· Sustainability· 0 citations