Abstract
The emergence of a new class of systems for monitoring and forecasting hazardous processes and natural phenomena, capable of taking into account information received from network users, actualizes the issues related to the collection, processing and transmission of large volumes of heterogeneous and semi-structured data from large geographically distributed territories. At the same time, one of the main problems is the integration and further analysis of such data in order to increase the level of decision-making automation. Ontological models make it possible to systematize and formalize semi-structured data, to obtain knowledge about the domain on their basis. This paper considers the architecture of a system for monitoring and forecasting hazardous natural processes, including an intellectual analysis module. It is proposed to use ontology models to determine the vertices of a cognitive map, on the basis of which further analysis will be carried out and a decision will be made. In this regard, the purpose of this study is to determine the vertices of the cognitive map of the aquatic ecosystem of the Ponto-Caspian region. To achieve this goal, it is necessary to develop domain ontologies that reflect knowledge about the domain of hazardous processes and natural phenomena of the coastal infrastructure of the Ponto-Caspian region, as well as knowledge taking into account information received from network users for a monitoring and forecasting system.
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This study is supported by the by the RFBR project 18-29-22046, 20-04-60485 and the GZ SSC RAS 122020100270-3.
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Safronenkova, I.B., Rodina, A.A., Melnik, Y.E. (2022). Ontological Models as Components of the Decision Support Subsystem in Monitoring and Forecasting Systems of Hazardous Processes and Natural Phenomena of Coastal Infrastructure. In: Silhavy, R. (eds) Artificial Intelligence Trends in Systems. CSOC 2022. Lecture Notes in Networks and Systems, vol 502. Springer, Cham. https://doi.org/10.1007/978-3-031-09076-9_19
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