Luisa Lavalle

Assessing cascading effects of natural hazards on critical infrastructures requires the coordination of multiple methods and integration of simulation results from diverse tools. This work presents an integrated methodology and a simulation framework for instantiating and enriching risk scenarios through data-driven impact analyses. The approach builds on the semantic storylines concept, i.e., formal, machine-readable representations of risk storylines rather than narrative descriptions. These are instantiated as semantic data storylines by means of a virtual knowledge graph capturing the key entities and relationships for the scenario analysis and interface with domain simulators. The resulting machine-readable structures allow for traceability of assumptions and results, and reuse of knowledge across applications. Its experimentation benefits from the ReturnVille virtual testbed, developed within the RETURN project, which enables the use of hazard maps and the configuration of detailed scenarios defined through impact chains and applied to representative networks. A domain simulator enabled accurate analysis of a drinking water network and impact quantification, while semantic data storylines structured the simulation inputs, with the resulting outputs informing scenario verification and evolution.