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This study investigates the implementation of alert systems for critical infrastructures within their operational environment. The challenge arises from the extensive scale of these infrastructures, the heterogeneity and complexity of the data sources, and the imperative for real-time responsiveness. To address these challenges, we propose data-driven methodologies that demonstrate the feasibility of such systems. Specifically, we examine two complementary case studies: 1) the detection of sinkholes in railway networks and 2) the development of a metamodel for seismic risk assessment in dam infrastructure. These case studies illustrate the pivotal role of artificial intelligence and predictive analytics in enhancing alert systems and facilitating proactive risk management. The proposed approach contributes to the field of critical infrastructure monitoring by integrating advanced data-driven strategies, thereby reinforcing its scientific relevance and novelty.

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Data-Driven Models for Alert Management in Infrastructure Monitoring Within Their Environment

  • 会议论文

  • 发表日期：

    2025年

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  • Fakhreddine Ababsa,
  • Maryem Bouali,
  • Mohamad Ali Noureddine,
  • Rani El Meouche,
  • Florent De Martin,
  • Bahar Salvati

摘要

This study investigates the implementation of alert systems for critical infrastructures within their operational environment. The challenge arises from the extensive scale of these infrastructures, the heterogeneity and complexity of the data sources, and the imperative for real-time responsiveness. To address these challenges, we propose data-driven methodologies that demonstrate the feasibility of such systems. Specifically, we examine two complementary case studies: 1) the detection of sinkholes in railway networks and 2) the development of a metamodel for seismic risk assessment in dam infrastructure. These case studies illustrate the pivotal role of artificial intelligence and predictive analytics in enhancing alert systems and facilitating proactive risk management. The proposed approach contributes to the field of critical infrastructure monitoring by integrating advanced data-driven strategies, thereby reinforcing its scientific relevance and novelty.