A Generative AI-Driven Reliability Layer for Action-Oriented Disaster Resilience

• URL: http://arxiv.org/abs/2601.18308v1
• Date: Mon, 26 Jan 2026 09:43:30 GMT
• Title: A Generative AI-Driven Reliability Layer for Action-Oriented Disaster Resilience
• Authors: Geunsik Lim,
• Abstract summary: Climate RADAR (Risk-Aware, Dynamic, and Action Recommendation system) is a generative AI-based reliability layer that reframes disaster communication from alerts delivered to actions executed.<n>It integrates meteorological, hydrological, vulnerability, and social data into a composite risk index and employs guardrail-embedded large language models (LLMs) to deliver personalized recommendations across citizen, volunteer, and municipal interfaces.<n> Evaluation through simulations, user studies, and a municipal pilot shows improved outcomes, including higher protective action execution, reduced response latency, and increased usability and trust.
• Score: 0.6768558752130311
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As climate-related hazards intensify, conventional early warning systems (EWS) disseminate alerts rapidly but often fail to trigger timely protective actions, leading to preventable losses and inequities. We introduce Climate RADAR (Risk-Aware, Dynamic, and Action Recommendation system), a generative AI-based reliability layer that reframes disaster communication from alerts delivered to actions executed. It integrates meteorological, hydrological, vulnerability, and social data into a composite risk index and employs guardrail-embedded large language models (LLMs) to deliver personalized recommendations across citizen, volunteer, and municipal interfaces. Evaluation through simulations, user studies, and a municipal pilot shows improved outcomes, including higher protective action execution, reduced response latency, and increased usability and trust. By combining predictive analytics, behavioral science, and responsible AI, Climate RADAR advances people-centered, transparent, and equitable early warning systems, offering practical pathways toward compliance-ready disaster resilience infrastructures.

Related papers

• Forecasting Fails: Unveiling Evasion Attacks in Weather Prediction Models [60.728124907335]
  This work introduces Weather Adaptive Adversarial Perturbation Optimization (WAAPO), a novel framework for generating targeted adversarial perturbations.<n>WAAPO achieves this by incorporating constraints for channel sparsity, spatial localization, and smoothness, ensuring that perturbations remain physically realistic and imperceptible.<n>Our experiments highlight critical vulnerabilities in AI-driven forecasting models, where small perturbations to initial conditions can result in significant deviations.
  arXiv  Detail & Related papers  (2025-12-09T17:20:56Z)
• Agentic AI Framework for Cloudburst Prediction and Coordinated Response [0.8697317909540486]
  The paper outlines an agentic artificial intelligence system to study atmospheric water-cycle intelligence.<n>The framework uses autonomous but cooperative agents that reason, sense, and act throughout the entire event lifecycle.<n>It provides a platform of scalable adaptive and learning-based climate resilience.
  arXiv  Detail & Related papers  (2025-11-27T21:33:03Z)
• ANNIE: Be Careful of Your Robots [48.89876809734855]
  We present the first systematic study of adversarial safety attacks on embodied AI systems.<n>We show attack success rates exceeding 50% across all safety categories.<n>Results expose a previously underexplored but highly consequential attack surface in embodied AI systems.
  arXiv  Detail & Related papers  (2025-09-03T15:00:28Z)
• Situational Awareness as the Imperative Capability for Disaster Resilience in the Era of Complex Hazards and Artificial Intelligence [1.1368819707015188]
  Disasters frequently exceed established hazard models, revealing blind spots where unforeseen impacts and vulnerabilities hamper effective response.<n> situational awareness (SA)-the ability to perceive, interpret, and project dynamic crisis conditions-is an often overlooked yet vital capability for disaster resilience.
  arXiv  Detail & Related papers  (2025-08-21T03:38:47Z)
• Interpretable Dual-Stream Learning for Local Wind Hazard Prediction in Vulnerable Communities [1.9299285312415735]
  Wind hazards such as tornadoes and straight-line winds frequently affect vulnerable communities in the Great Plains of the United States.<n>Existing forecasting systems focus primarily on meteorological elements and often fail to capture community-specific vulnerabilities.<n>We propose a dual-stream learning framework that integrates structured numerical weather data with unstructured textual event narratives.<n>Our architecture combines a Random Forest and RoBERTa-based transformer through a late fusion mechanism, enabling robust and context-aware wind hazard prediction.
  arXiv  Detail & Related papers  (2025-05-20T15:46:02Z)
• EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.<n>Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.<n>However, the deployment of these agents in physical environments presents significant safety challenges.<n>This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
  FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
  arXiv  Detail & Related papers  (2024-03-26T08:51:23Z)
• Towards an end-to-end artificial intelligence driven global weather forecasting system [57.5191940978886]
  We present an AI-based data assimilation model, i.e., Adas, for global weather variables. We demonstrate that Adas can assimilate global observations to produce high-quality analysis, enabling the system operate stably for long term. We are the first to apply the methods to real-world scenarios, which is more challenging and has considerable practical application potential.
  arXiv  Detail & Related papers  (2023-12-18T09:05:28Z)
• Data-driven recommendations for enhancing real-time natural hazard warnings, communication, and response [0.0]
  This Perspective reviews existing data-driven approaches that underpin real-time warning communication and emergency response. Four main themes for enhancing warnings are emphasised. Motivating examples are provided from the extensive flooding experienced in Australia in 2022.
  arXiv  Detail & Related papers  (2023-11-01T02:59:45Z)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.