Ontology Platform Routing Disaster Information and Data for Decision Making

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.