
Abstract This article proposes a framework of linked software agents that continuously interact with an underlying knowledge graph to automatically assess the impacts of potential flooding events. It builds on the idea of connected digital twins based on the World Avatar dynamic knowledge graph to create a semantically rich asset of data, knowledge, and computational capabilities accessible to humans, applications, and artificial intelligence. We develop three new ontologies to describe and link environmental measurements and their respective reporting stations, flood events, and their potential impact on population and built infrastructure as well as the built environment of a city itself. These coupled ontologies are deployed to dynamically instantiate near real-time data from multiple fragmented sources into the World Avatar. Sequences of autonomous agents connected via the derived information framework automatically assess consequences of newly instantiated data, such as newly raised flood warnings, and cascade respective updates through the graph to ensure up-to-date insights into the number of people and building stock value at risk. Although we showcase the strength of this technology in the context of flooding, our findings suggest that this system-of-systems approach is a promising solution to build holistic digital twins for various other contexts and use cases to support truly interoperable and smart cities.
flood assessment, derived information framework, Bioengineering, Engineering (General). Civil engineering (General), 4605 Data Management and Data Science, Networking and Information Technology R&D (NITRD), knowledge graph, 46 Information and Computing Sciences, 4602 Artificial Intelligence, digital twin, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD), ontology, TA1-2040
flood assessment, derived information framework, Bioengineering, Engineering (General). Civil engineering (General), 4605 Data Management and Data Science, Networking and Information Technology R&D (NITRD), knowledge graph, 46 Information and Computing Sciences, 4602 Artificial Intelligence, digital twin, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD), ontology, TA1-2040
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 16 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
