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
Both natural and man-made calamities have major impacts to life, infrastructures, and economic stability across the globe. The rising rate and severity of catastrophes like earthquakes, floods, cyclones, landslides, wildfires and pandemics require the creation of sophisticated forecasting methods. Conventional ways of managing disasters tend to use isolated data sources and manual interpretation thereby providing restrictions on predictive accuracy and response frequency. The paper is 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. The proposed model optimizes the situational awareness and early warning capabilities by using machine and deep learning. The research presents a multi-layered data acquisition, data preprocessing, and data trending, feature extraction, and predictive modeling. Sir-complicated algorithms that include the use of random forest, support vector machines, Convolutional Neural Networks, and Long Short-term Memory networks are used to process spatio-temporal patterns. The real-world datasets were used to test the system and show better prediction accuracy and lower response time. Findings from experimental outcomes show that holistic data-driven systems work far much better than traditional single source methodologies. The results point out the significance of data integration and smart analytics in disaster risk management. The study will help develop resilient smart disaster management systems and form a basis on the future advancements in real-time forecasting and emergency response planning.
Emma Roberts· International Journal of Eme...· 0 citations
Effective flood risk management relies on accurate forecasting, yet the"black box"nature of stateof-the-art Deep Learning models creates a barrier to trust and accountability in high-stakes public safety decisions. While existing Explainable AI (XAI) methods offer local attributions, they fail to provide the verifiable, operationally meaningful causal narratives required by disaster response authorities. To address this societal challenge, we propose Context-Aware Concept Distillation (CACD), a framework developed in collaboration with domain experts to distill opaque LSTMs into interpretable, hydrology-aware surrogate models. We introduce an unsupervised pipeline to discover a"Hydrological Language"and a Residual Hypernetwork that dynamically modulates these concepts based on static basin characteristics. Evaluated on 5,203 basins globally, our model achieves high fidelity (Median NSE 0.70), significantly outperforming black-box baselines (e.g., Multi Layer Perceptrons) on unseen future data. By demonstrating that human-interpretable concepts are sufficient to reconstruct flood dynamics, this work balances AI accuracy with the transparency required for responsible environmental decision-making.
Eli Levinkopf, E. Morin, Claudia V. Goldman· 0 citations
Natural and human-induced disasters are increasing in frequency and severity due to climate change, rapid urbanization, environmental degradation, and population growth. Conventional disaster prediction methods often lack the speed and accuracy needed for real-time emergency response. Recent advances in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Big Data Analytics enable intelligent systems to analyze diverse real-time data from satellites, IoT sensors, weather stations, seismic networks, GIS, and social media for accurate disaster forecasting. This paper presents an AI-based decision support framework integrating data acquisition, preprocessing, feature engineering, machine learning, deep learning, and automated decision-making within a scalable cloud-edge architecture. The study also reviews existing AI-based disaster prediction approaches, identifies their limitations, and compares their performance. 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
Both natural and man-made disasters are very impactful to the surroundings, infrastructure, and human life. Proper and timely forecasting of disasters is essential in the reduction of the disaster. The conventional disaster prediction models are based on analysis of previous history and simple statistical methods which could be not capable of offering real-time and adaptive decision-making options. The present paper includes an in-depth research concerning AI-based decision systems of real-time disaster predictions that combine the latest machine learning (ML), deep learning (DL), and real-time sensor networks. We suggest 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. The system resorts to ensemble learning, recurrent neural networks (RNNs), and spatiotemporal modeling and manages to predict the occurrence of a flood, earthquake, wildfire, and storm with high accuracy. The system is shown in one of the case studies that use actual real-time sensor data on the environment and satellite images to prove the efficiency of the system. It is shown that there is substantial increase in accuracy of prediction and response time over traditional systems. The given strategy is focused on scalability, flexibility, and resilience to different disaster risks. In addition, the combination of AI and Internet of Things (IoT) and Geographic Information System (GIS) allows developing a real-time decision support system that can support the government agencies, emergency responders, and communities with making proactive and data-driven decisions. The study indicates the possibilities of AI-powered systems in shifting disaster management to a predictive instead of a reactive and prevention system.
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