Stavrou, M.
In the last decade, Cyprus has faced a sequence of extreme wildfire events—the catastrophic 2016 fire in the island’s largest forest ecosystem, the fatal 2021 wildfire in the mountainous region of Larnaca, and the unprecedented 2025 megafire in the Limassol highlands—each exposing structural gaps in national prevention, response, and post-disaster recovery mechanisms. These events underscored a critical insight: addressing contemporary wildfire risk requires coordinated action that transcends traditional institutional boundaries.
Within this context, the SupportCY initiative of the Bank of Cyprus, originally founded in 2020 as a national solidarity platform during the COVID-19 crisis, has evolved into a globally unique tripartite network that formally integrates state authorities, private-sector organisations, and academic/research institutions into a unified framework for crisis management, wildfire resilience, and civil protection.
Today, SupportCY operates as the only known international model in which over two hundred entities—including ministries, emergency services, universities, research centres, private companies, community councils, and volunteer units—collaborate systematically on wildfire prevention, operational readiness, and long-term recovery.
This integrated structure enables multi-layered interventions: development and deployment of training programmes for citizens and frontline responders; establishment of the National Bee Reproduction Centre in fire-affected zones to restore ecological functions and support local livelihoods; scientific assessments and redesign proposals for the reconstruction of critical infrastructure and high-risk communities; provision of psychosocial support services for families and children; and active participation in European research and innovation programmes aimed at enhancing wildfire intelligence, digital resilience, and the capabilities of professional and volunteer first responders. Additionally, SupportCY operates its own specialised Volunteer Corps, equipped with trained responders and firefighting vehicles, officially recognised by both the Cyprus Fire Service and the Hellenic Fire Service.
The presentation will provide a comprehensive analysis of these collaborative initiatives, illustrating how they emerged through real-time operational demands, local community needs, and evidence-based scientific methodologies. It will further demonstrate how the SupportCY ecosystem has become a living laboratory of applied multi-stakeholder governance, capable of accelerating innovation, bridging research with field operations, and producing actionable solutions for the increasingly complex wildfire regimes of the Mediterranean. As the only global example of a structured, permanent, and operational state–private–academic partnership for wildfire resilience and civil protection, this case offers a replicable model for nations seeking to redesign their disaster-management architectures under the pressures of climate change.
SupportCY, Bank of Cyprus.
Presented at: European Geosciences Union (EGU) General Assembly 2026.
Venkatasubramanian, B. V., Laoudias, C., & Panteli, M.
Extreme windstorms pose significant risks to interconnected critical infrastructures such as power, transportation, and telecommunication systems. Wind-induced damage to physical assets, including overhead lines and roadside vegetation, can trigger cascading failures across interdependent networks, leading to widespread service disruptions and societal impacts. Anticipating these cascading effects under uncertain and evolving windstorm conditions remains a major challenge for emergency and crisis management.
An AI-powered Digital Twin (DT) framework for windstorm emergency management is introduced in this presentation, focusing on interconnected critical infrastructures exposed to extreme wind hazards. The framework integrates physics-based windstorm simulation with cascading impact analysis within a unified digital environment, enabling systematic assessment of the interconnected infrastructure performance across a wide range of plausible windstorm scenarios. Rather than relying solely on historical events, physically informed models are used to generate synthetic windstorm scenarios that support preparedness planning and stress-testing under future extreme conditions.
Building on ensembles of simulated windstorm scenarios, the framework can incorporate Generative AI (GenAI) techniques as a post-simulation analytical layer for vulnerability and risk analysis. GenAI operates on the outputs of physics-based simulations, learning asset-level and system-level operational behaviors and vulnerability patterns from simulated impacts, rather than replacing the underlying hazard or infrastructure models. In this role, GenAI captures complex and nonlinear relationships between wind event characteristics and cascading infrastructure failures, enabling efficient synthesis and generalization across large scenario ensembles. This hybrid physics–AI approach supports rapid and accurate identification of vulnerable assets across interconnected infrastructures, spatial hotspots of risk, and conditions that may lead to cascading disruptions under future windstorm scenarios, while preserving the physical consistency of the Digital Twin.
The applicability of the proposed framework is demonstrated through representative case studies involving national-scale interconnected power, telecommunication, and transportation infrastructures in Cyprus, serving as an example implementation. The results illustrate how the AI-powered Digital Twin can support emergency and crisis management at a national level by enabling stress-testing of infrastructure systems, identification of highly vulnerable and critical assets in the Cyprus interconnected infrastructure, improving situational awareness on critical wind-induced cascading risks, and informing response and recovery strategies under severe windstorm conditions.
Overall, this work highlights the potential of hybrid physics-based and AI-enhanced Digital Twins as decision-support tools for windstorm emergency management in interconnected critical infrastructures, providing a flexible and extensible foundation for improving resilience to climate-driven hazards.
KIOS Research and Innovation Center of Excellence, University of Cyprus, Nicosia, Cyprus.
Presented at: European Geosciences Union (EGU) General Assembly 2026.
Paphitis, G., & Panteli, M.
The frequency of cascading failures in power systems is rising, driven by factors such as high renewable energy penetration and extreme weather events. Cascading failure models (CFMs), static and dynamic, help analyze these mechanisms and improve resilience. Recently, machine learning (ML) approaches have emerged to rapidly quantify cascading impacts. This paper compares static and dynamic cascading simulators based on how effectively they generate informative data for training ML models to predict spatial and total load shedding. Furthermore, conformal prediction is applied to assess the trustworthiness of the ML models. Results show that ML models trained on dynamic CFMs achieve nearly three times lower mean absolute error and root mean squared error, with improved coverage and narrower prediction intervals.
KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, Cyprus.
IEEE Conference Publication.
Georgiou, P., Leventis, C., Isaia, P., Kyriakou, M., Venkatasubramanian, B. V., Vrachimis, S., Laoudias, C., Eliades, D., & Panteli, M.
Modern smart cities now place top priority on keeping Critical Infrastructure Systems (CIS) like power grids, water networks, transportation systems, and telecommunications both resilient and highly efficient. Recent advances in Digital Twin (DT) technology have made it an attractive tool for managing these systems by generating dynamic, data-driven virtual models of cyber-physical assets and their interconnections. Despite widespread adoption of DTs across many industries, there is no risk-based, scenario-driven DT framework readily available to researchers, smart city operators, and CIS managers for bolstering urban resilience. To fill this gap, we present the Cyprus Digital Twin (CyDT), a one-of-a-kind digital platform designed to manage and optimize interdependent CIS. We detail CyDT's modular architecture outlining its principal components and spotlighting its novel features and capabilities and demonstrate its adaptability and effectiveness in real-life use cases that conduct risk-based resilience analyses of power and water systems.
KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, Cyprus.
IEEE Conference Publication.
Venkatasubramanian, B. V., Laoudias, C., & Panteli, M.
Natural hazards, such as storms, increasingly threaten interconnected critical infrastructure systems (CISs), necessitating integrated resilience strategies that account for interdependencies, cascading failures, and dynamic recovery. To contextualize the research within existing developments, a bibliometric analysis is conducted, revealing key trends and gaps in hazard-resilient CIS modeling. This article proposes a comprehensive framework that models interdependent behavior across power, telecommunication, and transportation networks under evolving storm conditions. A dedicated hazard engine captures the spatiotemporal progression of storms and simulates their direct and cascading impacts across infrastructure layers. The framework incorporates a dynamic, safety-aware restoration strategy that deploys repair crews and mobile energy resources in alignment with real-time storm conditions. The proposed framework is demonstrated through a case study inspired by the realistic infrastructure of Cyprus. Results show the model's ability to capture cascading disruptions, illustrate the operational effectiveness of dynamic restoration, and provide insights from sensitivity and tradeoff analyses.
KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, Cyprus.
IEEE Conference Publication.
Venkatasubramanian, B. V., Laoudias, C., & Panteli, M.
Extreme weather events increasingly threaten critical infrastructure (CI) systems, particularly power and telecommunication networks, leading to cascading failures. This paper proposes a novel resilience assessment framework that explicitly models spatial interdependencies between electricity and telecommunication networks using a graph-based approach. Sectorspecific metrics, including Demand Not Served (DNS), power line failure rates, and affected population estimates, quantify the impact of windstorm-induced disruptions. A spatiotemporal hazard scenario generator simulates realistic storm events, dynamically propagating failures through the coupled networks. A key innovation is the integration of a telecommunication signal propagation model-combining Extended Hata, Rapport models, and a binary search algorithm-to estimate coverage loss within the resilience framework. The framework is validated in Cyprus using real-world and synthetic data, identifying vulnerable regions and informing disaster response strategies.
KIOS Research and Innovation Center of Excellence, Department of Electrical and Computer Engineering, University of Cyprus, Cyprus.
IEEE Conference Publication.