A Civil Protection Early Warning System to Improve the Resilience of Adriatic-Ionian Territories to Natural and Man-made Risk

• URL: http://arxiv.org/abs/2207.13941v1
• Date: Thu, 28 Jul 2022 08:05:37 GMT
• Title: A Civil Protection Early Warning System to Improve the Resilience of Adriatic-Ionian Territories to Natural and Man-made Risk
• Authors: Agorakis Bompotas, Christos Anagnostopoulos, Athanasios Kalogeras, Georgios Kalogeras, Georgios Mylonas, Kyriakos Stefanidis, Christos Alexakos, Miranda Dandoulaki
• Abstract summary: 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.
• Score: 3.981878112335394
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We are currently witnessing an increased occurrence of extreme weather events, causing a great deal of disruption and distress across the globe. In this setting, the importance and utility of Early Warning Systems is becoming increasingly obvious. In this work, 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, addressing natural and man-made risks from the early warning stage and improving the intervention capabilities of civil protection mechanisms. The system utilizes an innovative approach with a lever effect, while also aiming to support the whole system of Civil Protection.

Related papers

• Multi-Hazard Early Warning Systems for Agriculture with Featural-Temporal Explanations [5.363664265121231]
  Climate extremes present escalating risks to agriculture.<n>Traditional single-hazard forecasting methods fall short in capturing complex interactions among concurrent climatic events.<n>In this paper, we combine sequential deep learning models and advanced Explainable Artificial Intelligence (XAI) techniques to introduce a multi-hazard forecasting framework for agriculture.
  arXiv  Detail & Related papers  (2025-07-30T05:16:35Z)
• AI-based Approach in Early Warning Systems: Focus on Emergency Communication Ecosystem and Citizen Participation in Nordic Countries [0.7373617024876725]
  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.
  arXiv  Detail & Related papers  (2025-06-20T12:32:16Z)
• An Approach to Technical AGI Safety and Security [72.83728459135101]
  We develop an approach to address the risk of harms consequential enough to significantly harm humanity. We focus on technical approaches to misuse and misalignment. We briefly outline how these ingredients could be combined to produce safety cases for AGI systems.
  arXiv  Detail & Related papers  (2025-04-02T15:59:31Z)
• AI threats to national security can be countered through an incident regime [55.2480439325792]
  We propose a legally mandated post-deployment AI incident regime that aims to counter potential national security threats from AI systems. Our proposed AI incident regime is split into three phases. The first phase revolves around a novel operationalization of what counts as an 'AI incident' The second and third phases spell out that AI providers should notify a government agency about incidents, and that the government agency should be involved in amending AI providers' security and safety procedures.
  arXiv  Detail & Related papers  (2025-03-25T17:51:50Z)
• Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI [52.138044013005]
  generative AI, particularly large language models (LLMs), become increasingly integrated into production applications. New attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal systems. Red-teaming has gained importance in proactively identifying weaknesses in these systems, while blue-teaming works to protect against such adversarial attacks. This work aims to bridge the gap between academic insights and practical security measures for the protection of generative AI systems.
  arXiv  Detail & Related papers  (2024-09-23T10:18:10Z)
• AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies [88.32153122712478]
  We identify 314 unique risk categories organized into a four-tiered taxonomy. At the highest level, this taxonomy encompasses System & Operational Risks, Content Safety Risks, Societal Risks, and Legal & Rights Risks. We aim to advance AI safety through information sharing across sectors and the promotion of best practices in risk mitigation for generative AI models and systems.
  arXiv  Detail & Related papers  (2024-06-25T18:13:05Z)
• Global Mapping of Exposure and Physical Vulnerability Dynamics in Least Developed Countries using Remote Sensing and Machine Learning [0.7237068561453082]
  This paper aims to advance the area of large-scale risk quantification in informing our collective long-term efforts in reducing climate and disaster risk. We present an ongoing effort in mapping this vital information using machine learning and time-series remote sensing from publicly available Sentinel-1 SAR GRD and Sentinel-2 Harmonized MSI. We introduce the development of "OpenSendaiBench" consisting of 47 countries wherein most are least developed (LDCs), trained ResNet-50 deep learning models, and demonstrated the region of Dhaka, Bangladesh.
  arXiv  Detail & Related papers  (2024-04-02T09:04:56Z)
• Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications [69.13807233595455]
  Large language models (LLMs) show inherent brittleness in their safety mechanisms. This study explores this brittleness of safety alignment by leveraging pruning and low-rank modifications. We show that LLMs remain vulnerable to low-cost fine-tuning attacks even when modifications to the safety-critical regions are restricted.
  arXiv  Detail & Related papers  (2024-02-07T18:34:38Z)
• 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)
• Temporal Analysis of World Disaster Risk:A Machine Learning Approach to Cluster Dynamics [0.0]
  This paper assesses the impact of efforts considered to mitigate risk and create safe environments on a global scale. Using the World Risk Index, we conduct a temporal analysis of global disaster risk dynamics from 2011 to 2021.
  arXiv  Detail & Related papers  (2024-01-10T08:50:53Z)
• 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)
• AI Increases Global Access to Reliable Flood Forecasts [6.498815455353096]
  Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks. Using AI, we achieve reliability in predicting extreme riverine events in ungauged watersheds at up to a 5-day lead time. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings.
  arXiv  Detail & Related papers  (2023-07-30T01:49:21Z)
• The Challenges and Opportunities in Creating an Early Warning System for Global Pandemics [0.0]
  COVID-19 pandemic revealed that global health, social systems, and economies can be surprisingly fragile in an increasingly interconnected and interdependent world. We can demonstrate how thoughtfully designed technology platforms can build global-scale precision disease detection and response systems.
  arXiv  Detail & Related papers  (2023-02-02T04:18:47Z)
• PreDisM: Pre-Disaster Modelling With CNN Ensembles for At-Risk Communities [0.32228025627337864]
  We introduce PreDisM that employs an ensemble of ResNets and fully connected layers over decision trees to accurately estimate weakness of man-made structures to disaster-occurrences. Our model performs well and is responsive to tuning across types of disasters and highlights the space of preemptive hazard damage modelling.
  arXiv  Detail & Related papers  (2021-12-26T23:48:23Z)
• Digital Ariadne: Citizen Empowerment for Epidemic Control [55.41644538483948]
  The COVID-19 crisis represents the most dangerous threat to public health since the H1N1 pandemic of 1918. Technology-assisted location and contact tracing, if broadly adopted, may help limit the spread of infectious diseases. We present a tool, called 'diAry' or 'digital Ariadne', based on voluntary location and Bluetooth tracking on personal devices.
  arXiv  Detail & Related papers  (2020-04-16T15:53:42Z)
• 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.