Jun 2026· 2026 5th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0· pp. 1-6· 0 citations· 11 references
Abstract
These constitute the most salient issues in disaster preparedness which are rapid evaluation of the dangers and judicious application of precautionary warnings. Traditional models, that utilise the physical implementation of the sensor webs and that typically use inflexible threshold logic, typically lack the structuring to query dynamic situational contexts or give results in a subtle way of interpretation. This paper will present a softwarefocused model of disaster management wherein me-teorological, media, crowdsourced social, and archival coverage of the disaster are combined into an integrated analysis channel. The scoring scheme is deterministic and produces a Composite Risk Index by the algorithmic combination of all streams of data, a big language model then provides post-hoc, natural language explanations in order to justify danger ratings. The modern risk environments are modelled in the interactive dashboard which is designed based on latest web-based technologies and sends directive advisories. Simulations of multi-hazard scenarios with the use of empirical trials identify a classification accuracy of 91.4 percent and end-to-end processing time that is an average of twelve seconds, thus proving the applicability of the system in operational conditions limited by small resources, damaged infrastructure, or inaccurate sensors.
This paper presents a modular decision support system that infers the primary location of the user or the reported incident and a situation-aware risk level from multi-turn Turkish disaster dialogues between a help-seeking user and an AI-supported emergency assistant. The assistant guides the user with follow-up questions about health status, number of affected people, structural damage, environmental hazards, and known nearby landmarks to complete missing information. The system manages the dialogue with a finite state machine, determines the location by linking user cues to a local GeoJSON point-of-interest database and by landmark verification, and produces explainable risk scores with Multi-Criteria Decision Analysis. The key novelty is treating landmarks as an evidence layer that verifies the current location hypothesis through proximity and clustering instead of directly replacing candidates based on a landmark signal. In a ten-scenario pilot evaluation, accuracy, end-to-end latency, and token usage are reported for three configurations.
Eren Varlıker, Yusuf Sinan Özmen, Selim Balcisoy· Signal Processing and Commun...· 0 citations
A full predictive framework of disasters based on the integrated data system as a conglomeration of satellite imagery, Internet of Things (IoT) sensor data, meteorological data, geospatial databases, social media feeds and historic disaster data is presented.
Emma Roberts· International Journal of Eme...· 0 citations
Context-Aware Concept Distillation (CACD) is proposed, a framework developed in collaboration with domain experts to distill opaque LSTMs into interpretable, hydrology-aware surrogate models and a Residual Hypernetwork that dynamically modulates these concepts based on static basin characteristics.
Eli Levinkopf, E. Morin, Claudia V. Goldman· 0 citations
The findings demonstrate that AI-driven disaster prediction systems significantly improve early warning capabilities, situational awareness, resource allocation, infrastructure protection, and emergency response, ultimately reducing disaster impacts and saving lives.
Alan Bundy, Karen Spärck Jones· International Journal of Mod...· 0 citations
An approach based on multi-layered which involves combining real-time data collection, AI-enhanced predictive analytics, and automated decision-making to improve disaster preparedness and response is suggested to improve disaster preparedness and response.
Pooja Agarwal· International Journal of Mod...· 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