AI-based Approach in Early Warning Systems: Focus on Emergency Communication Ecosystem and Citizen Participation in Nordic Countries

• URL: http://arxiv.org/abs/2506.18926v1
• Date: Fri, 20 Jun 2025 12:32:16 GMT
• Title: AI-based Approach in Early Warning Systems: Focus on Emergency Communication Ecosystem and Citizen Participation in Nordic Countries
• Authors: Fuzel Shaik, Getnet Demil, Mourad Oussalah,
• Abstract summary: The chapter advocates a holistic approach, distinguishing preparedness, emergency responses, and postcrisis phases.<n>The role of the Early Warning System (EWS), Risk modeling and mitigation measures are particularly emphasized.<n>Case studies from Nordic countries have been highlighted.
• Score: 0.7373617024876725
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Climate change and natural disasters are recognized as worldwide challenges requiring complex and efficient ecosystems to deal with social, economic, and environmental effects. This chapter advocates a holistic approach, distinguishing preparedness, emergency responses, and postcrisis phases. The role of the Early Warning System (EWS), Risk modeling and mitigation measures are particularly emphasized. The chapter reviews the various Artificial Intelligence (AI)-enabler technologies that can be leveraged at each phase, focusing on the INFORM risk framework and EWSs. Emergency communication and psychological risk perception have been emphasized in emergency response times. Finally, a set of case studies from Nordic countries has been highlighted.

Related papers

• The Problem of Algorithmic Collisions: Mitigating Unforeseen Risks in a Connected World [2.8775022881551666]
  The increasing deployment of Artificial Intelligence (AI) and other autonomous algorithmic systems presents the world with new systemic risks.<n>Current governance frameworks are inadequate as they lack visibility into this complex ecosystem of interactions.<n>This paper outlines the nature of this challenge and proposes some initial policy suggestions centered on increasing transparency and accountability through phased system registration, a licensing framework for deployment, and enhanced monitoring capabilities.
  arXiv  Detail & Related papers  (2025-05-26T16:22:18Z)
• AI and Generative AI Transforming Disaster Management: A Survey of Damage Assessment and Response Techniques [1.4513830934124627]
  We present a review on the prospects of AI and GenAI in damage assessment for various natural disasters.<n>We talk about its application to multimodal data such as text, image, video, and audio, and also cover major issues of data privacy, security, and ethical use of the technology during crises.<n>We outline avenues of future research, emphasizing the need for secure, reliable, and ethical Generative AI systems for disaster management in general.
  arXiv  Detail & Related papers  (2025-05-13T03:33:31Z)
• From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate [69.05573887799203]
  We argue that understanding these second-order impacts requires an interdisciplinary approach, combining lifecycle assessments with socio-economic analyses.<n>We contend that a narrow focus on direct emissions misrepresents AI's true climate footprint, limiting the scope for meaningful interventions.
  arXiv  Detail & Related papers  (2025-01-27T22:45:06Z)
• Global Perspectives of AI Risks and Harms: Analyzing the Negative Impacts of AI Technologies as Prioritized by News Media [3.2566808526538873]
  AI technologies have the potential to drive economic growth and innovation but can also pose significant risks to society.<n>One way to understand these nuances is by looking at how the media reports on AI.<n>We analyze a broad and diverse sample of global news media spanning 27 countries across Asia, Africa, Europe, Middle East, North America, and Oceania.
  arXiv  Detail & Related papers  (2025-01-23T19:14:11Z)
• Harnessing Large Language Models for Disaster Management: A Survey [57.00123968209682]
  Large language models (LLMs) have revolutionized scientific research with their exceptional capabilities and transformed various fields.<n>This study aims to guide the professional community in developing advanced LLMs for disaster management to enhance the resilience against natural disasters.
  arXiv  Detail & Related papers  (2025-01-12T21:00:50Z)
• 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)
• Mapping the individual, social, and biospheric impacts of Foundation Models [0.39843531413098965]
  This paper offers a critical framework to account for the social, political, and environmental dimensions of foundation models and generative AI. We identify 14 categories of risks and harms and map them according to their individual, social, and biospheric impacts.
  arXiv  Detail & Related papers  (2024-07-24T10:05:40Z)
• Risks of AI Scientists: Prioritizing Safeguarding Over Autonomy [65.77763092833348]
  This perspective examines vulnerabilities in AI scientists, shedding light on potential risks associated with their misuse.<n>We take into account user intent, the specific scientific domain, and their potential impact on the external environment.<n>We propose a triadic framework involving human regulation, agent alignment, and an understanding of environmental feedback.
  arXiv  Detail & Related papers  (2024-02-06T18:54:07Z)
• Managing extreme AI risks amid rapid progress [171.05448842016125]
  We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems. There is a lack of consensus about how exactly such risks arise, and how to manage them. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
  arXiv  Detail & Related papers  (2023-10-26T17:59:06Z)
• Predictable Artificial Intelligence [77.1127726638209]
  This paper introduces the ideas and challenges of Predictable AI.<n>It explores the ways in which we can anticipate key validity indicators of present and future AI ecosystems.<n>We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems.
  arXiv  Detail & Related papers  (2023-10-09T21:36:21Z)
• Human-AI Coevolution [48.74579595505374]
  Coevolution AI is a process in which humans and AI algorithms continuously influence each other. This paper introduces Coevolution AI as the cornerstone for a new field of study at the intersection between AI and complexity science.
  arXiv  Detail & Related papers  (2023-06-23T18:10:54Z)
• A Civil Protection Early Warning System to Improve the Resilience of Adriatic-Ionian Territories to Natural and Man-made Risk [3.981878112335394]
  We present the design of an early warning system called TransCPEarlyWarning, aimed at seven countries in the Adriatic-Ionian area in Europe. The overall objective is to increase the level of cooperation among national civil protection institutions in these countries.
  arXiv  Detail & Related papers  (2022-07-28T08:05:37Z)

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.