Jul 2026· Signal Processing and Communications Applications Conference· pp. 1-4· 0 citations· 10 references
Abstract
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.
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 structuri...
Rudrala Jahnavi, K. S. Upendra, M. Mujeebuddin et al.· 2026 5th OPJU International...· 0 citations
A Multi-Agent Disaster Management Simulator that automates the disaster response process using intelligent software agents, machine learning, graph-based routing, and generative artificial intelligence.
Hemanth S, Manoj M, Harisha S, Dr Manjunath B· International Journal of Adv...· 0 citations
A context-aware SAR framework was developed in this study to identify and estimate key parameters in rescue operations, activities, and environmental conditions, and to model the problem within a spatial information system, by integrating ant colony optimization (ACO) with the SARSA reinforcement learning algorithm.
N. Bahrami, Meysam Argany, A. D. Boloorani et al.· International Journal of Eng...· 0 citations
Natural disasters with tremendous impacts on human lives, infrastructure, and ecosystems are frequent all over the world, which calls for intelligent, data-driven decision support systems for early diagnosis and effective crisis management. This paper demonstrates a Natural Disaster Diagnosis and Crisis Management Syst...
EL-Alfy A.E, Esmat Mona, Sakr Hagar· International Journal of Sci...· 0 citations
This work presents HASTE (High-speed Assessment and Satellite Tracking for Emergencies), a no-code web platform that lets analysts who are not machine learning engineers produce per-building damage maps from post-disaster satellite imagery, and describes the platform, methods, and the engineering that supports them.
Caleb Robinson, Anthony Ortiz, S. Nsutezo et al.· 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 sign...
R. Paper, Research Supervisor Prof, Dominique Ferraro· The social science· 0 citations
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