Knowledge graphs to support real‐time flood impact evaluation

Abstract

A digital map of the built environment is useful for a range of economic, emergency response, and urban planning exercises such as helping find places in app driven interfaces, helping emergency managers know what locations might be impacted by a flood or fire, and helping city planners proactively identify vulnerabilities and plan for how a city is growing. Since its inception in 2004, OpenStreetMap (OSM) sets the benchmark for open geospatial data and has become a key player in the public, research, and corporate realms. Following the foundations laid by OSM, several open geospatial products describing the built environment have blossomed including the Microsoft USA building footprint layer and the OpenAddress project. Each of these products use different data collection methods ranging from public contributions to artificial intelligence, and if taken together, could provide a comprehensive description of the built environment. Yet, these projects are still siloed, and their variety makes integration and interoperability a major challenge. Here, we document an approach for merging data from these three major open building datasets and outline a workflow that is scalable to the continental United States (CONUS). We show how the results can be structured as a knowledge graph over which machine learning models are built. These models can help propagate and complete unknown quantities that can then be leveraged in disaster management.

Analysis of risk propagation using the world trade network

“…In addition to the PPR presented in this study, there are various ways to measure long-range impacts. Graph embedding techniques [ 25 ], which have recently become popular, can be used to measure the influence between countries, and more accurate information can be estimated using a multilayer network or knowledge graph that handles multiple relationships simultaneously [ 26 ]. When an economic crisis is anticipated as a result of COVID-19, we hope that the combination of our method and precise mathematical modeling will serve as a driving force to overcome the current and other forthcoming crises.…”

Knowledge graphs: Introduction, history, and perspectives

“…2022) and the flood impact evaluation OKN (Johnson et al. 2022) reported in this issue. To the degree that the Wikidata KG is fully integrated into Wikipedia, the discrepancy of missing links in the example provided here would not be present.…”

“…Wikidata solves the problem of identifying inverse relationships through the relation definitions created by curators and by using inference made possible through a KG inference engine. More advanced forms of such inference are illustrated in the Environmental Intelligence OKN (Janowicz et al 2022) and the flood impact evaluation OKN (Johnson et al 2022) reported in this issue. To the degree that the Wikidata KG is fully integrated into Wikipedia, the discrepancy of missing links in the example provided here would not be present.…”

Reproducibility and replicability of flood models

“…Advancements in AI and graph theory have enabled representation of complex problems using knowledge graphs that help understand the connections between different process components and identify the datasets/ontologies required for integration to answer specific questions. Because of the complexity of floods as a process and an event, and their subsequent impacts, the next generation of research should also focus on developing knowledge graphs and ontologies in collaboration with stakeholder communities (Johnson et al, 2022). Such an approach will not only assist different stakeholder groups to derive outcomes as per their needs, but also require having a standard evaluation framework and data repository to increase the integration of datasets and use of open‐source global flood models.…”