EGU25-5199, updated on 14 Mar 2025
https://doi.org/10.5194/egusphere-egu25-5199
EGU General Assembly 2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Developing Ontology-Based Nuclear Accident Knowledge Base Part 1: Spatiotemporal Considerations in Background Comparison
Misa Yasumiishi1 and Thomas Bittner2
Misa Yasumiishi and Thomas Bittner
  • 1CH2M HILL BWXT West Valley, LLC, New York, U.S.A.
  • 2University at Buffalo, The State University of New York, U.S.A.

The world has experienced three major nuclear accidents (Chernobyl, Three Mile Island, and Fukushima). We have accumulated a vast amount of data from those accidents as well as from less severe nuclear accidents and incidents tracked in the nuclear industry. It is time for us to develop an open knowledge base that gathers and shares nuclear accident-related data and enables users to utilize those data to prevent future nuclear accidents and implement effective remediation measures when an accident occurs. However, nuclear-related data have peculiar challenges when it comes to understanding and organizing the data because of the behavior of radioisotopes. Having examined the nuclear accident literature, we identified four data axes that are important to organize nuclear accident data systemically. Those axes are: (1) historical time flow vs. nuclear decay (logarithmic radioactive decay), (2) temporal extent affected by radioactive decay vs. spatial extent affected by environmental processes, (3) contamination vs. non-contamination (background) comparison for which a comparison method is decided upon (1) and (2), and (4) radioactivity vs. dose (health risk inflicted on organisms) as a result of exposure to radioactivity. These four axes need to be considered in every step related to nuclear accidents.

For example, when a nuclear accident occurs, the first step is to assess the level of radioactive contamination in the nearby environment. Distinguishing the accident-derived radioactivity from the background radioactivity [axis 3] is crucial to implementing evacuation and remediation procedures and assessing health risks [axis 4]. However, comparing accident-driven radioactivity against the background activity is not straightforward. Identifying a proper background location and collecting a sample involves spatial and temporal considerations [axis 1, 2], such as sampling distance, direction, depth, sampling timing, sampling frequency, etc. So far, a data organization scheme for nuclear accidents has not been established despite the vast amount of accident/incident data accumulated and the possible severe implications of nuclear accidents to society.

We propose ontology as a tool that addresses the peculiar nature of nuclear data and systematically organizes the knowledge we have acquired on nuclear accidents. Ontology originated as a metaphysical study in the field of philosophy to study the properties and relations of all involved entities. With the help of ontology, we provide formal definitions of the entities within the nuclear accident knowledge domain and connect those entities with logical rules. In this study, as the first step of nuclear accident ontology building, we present an ontology-based conceptual model on background comparison for environmental radioactivity utilizing a well-established Basic Formal Ontology (BFO) for upper-level ontology and several ontology models that have been developed in the sub-fields of the nuclear and energy industries. The rule-based entity structure based on ontology will contribute to building a comprehensive knowledge base for nuclear accidents. Our aim is to build a knowledge base adaptable to machine learning that will shorten the time needed for nuclear accident data search and provide insights into the best practices to minimize the adverse effects on humans and the environment from nuclear accidents.

How to cite: Yasumiishi, M. and Bittner, T.: Developing Ontology-Based Nuclear Accident Knowledge Base Part 1: Spatiotemporal Considerations in Background Comparison, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5199, https://doi.org/10.5194/egusphere-egu25-5199, 2025.