Building climate resilience to High-Impact Low-Probability events: an AI-driven modelling approach for Venice’s critical functions network

Climate-related High-Impact Low-Probability (CR-HILP) events pose a growing challenge to urban systems worldwide, as climate change amplifies the intensity, frequency, and compound nature of extreme events. These hazards, while rare, can generate disproportionate and cascading impacts on infrastructures, socio-economic processes, and environmental systems, often exceeding design thresholds and overwhelming traditional risk management approaches. This research proposes an integrated, systems-based framework to assess and enhance resilience to CR-HILP events in the Metropolitan City of Venice, a uniquely vulnerable socio-ecological system characterized by high exposure to climate hazards, strong interdependencies among critical functions, and exceptional cultural and historical value.

The study conceptualizes systemic resilience through the lens of Critical Functions (CFs), defined as the essential infrastructural, socio-economic, and environmental services that underpin societal stability and sustainability. Building on Network Science and complex systems theory, the research models these CFs as an interconnected multi-layer network, capturing both horizontal (intra-system) and vertical (inter-system) dependencies. The methodological framework is structured around three interlinked research tasks.

The first task develops a multi-layer network representation of critical functions, integrating real-world data such as GIS layers and infrastructure topology. Network Science metrics are employed to identify structurally intrinsic critical nodes and links, while different sampling techniques are used to detect minimal failure sets and structural vulnerabilities capable of triggering static systemic collapse.

The second task introduces a scenario-driven stress testing framework to assess dynamic cascading risks under CR-HILP events. High-resolution spatial hazard data, socio-economic vulnerability indicators, and stakeholder-informed narrative scenarios are combined with Percolation Theory to simulate disruption propagation across interconnected layers. This approach explicitly accounts for non-linear dynamics, interdependencies, and compound hazards, enabling the identification of tipping points, fragile configurations, and early-warning indicators for systemic failure.

The third task focuses on adaptive and resilient network design by integrating Graph Neural Networks (GNNs), Deep Reinforcement Learning (DRL), and Game Theory. A learning-based framework is developed to simulate adaptive responses and optimize resilience-enhancing interventions, such as rerouting connections, reinforcing critical nodes and edges, decentralizing dependencies, and reallocating capacities. Actor–Critic reinforcement learning methods, combined with GNN-based representations, enable agents to learn reconfiguration strategies that balance robustness, efficiency, and implementation costs. Extensions toward Multi-Agent Reinforcement Learning (MARL) allow the exploration of cooperation, competition, and negotiation among decentralized actors.

At the current stage, the approach has been tested on a single network layer, focusing on the transportation system, to validate the learning framework and intervention strategies. Future developments will extend the analysis to multiple interconnected layers, enabling the assessment of adaptive responses and cascading effects arising from interactions among different Critical Functions. By unifying network modeling, stress testing, and adaptive learning within a single framework, this research advances the understanding of systemic risk and resilience in complex interconnected urban systems. The Venice case study serves as a transferable testbed, offering methodological insights applicable to other climate-exposed metropolitan regions. The proposed approach aims to support decision-makers with actionable tools for resilience planning under deep uncertainty, contributing to more robust, adaptive, and climate-resilient urban futures.