PERSPECTIVE INFORMATION TECHNOLOGIES FOR ESIMO MODERNIZATION

  • E. D. Viazilov All-Russian Research Institute for Hydrometeorological Information – World Data Centre of Roshydrome
  • D. A. Melnikov All-Russian Research Institute for Hydrometeorological Information – World Data Centre of Roshydromet
DOI 10.29006/1564-2291.JOR-2025.53(2).9
Keywords promising information technologies, data integration, digital twin, AI, ESIMO, knowledge bases

Abstract

The relevance of the work is determined by the need to solve problems within the UN Decade of Ocean Science for Sustainable Development, as well as modernization of the Unified State System of Information on the Situation in the World Ocean (ESIMO), http://esimo.ru. The purpose of the article is to inform readers about promising information technologies that are proposed to be applied in the field of oceanography. The state of development and application of information technologies that are either already used, or are in the development phase, or will be used in the tasks of developing hydrometeorological support for users is considered. The technologies for integrating heterogeneous and distributed data based on metadata and creating a digital twin on this basis are shown. Services for autonomous detection of natural hazards and extreme phenomena and warning about various marine threats to the population and business leaders presented. Methods and tools of artificial intelligence in the form of knowledge graphs for linking various types and kinds of data, knowledge bases for automating mass processing of oceanographic data are proposed. Due to the implementation of the proposed information technologies, the functionality of ESIMO will be significantly expanded, the level of automation of data processing from collection to decision-making will increase.

References


  1. AI in Meteorology. Industry 2022. Azati Company. 19 December 2022. https://azati.ai/artificial-intelligence-in-meteorology/?ysclid=m09woxlitg155305830.(access date: 26.08.2024).

  2. Andreichuk, A. P., and A. V. Gurko, 2022: Trends in artificial intelligence and robotics technologies in the Arctic: the Russian experience. J. Mining informational and analytical bulletin, 10-2, 24–38.

  3. Artificial Intelligence applied to weather forecasting. https://ict.moscow/case/af7945dacf2b637c18d37470/?ysclid=m09v5m8q1c177429719/ (access date: 26.08.2024).

  4. ASUNP, 2024: Avtomatizirovannaya sistema ucheta nablyudatelnykh podrazdeleniy Rosgidrometa (ASUNP – Automated system of accounting of observation units of Roshydromet), http://asunp.meteo.ru.(last accessed in 12.08.2024).

  5. Boucher Philip, 2020: Artificial intelligence: How does it work, why does it matter, and what can we do about it? Study Panel for the Future of Science and Technology. European Parliament. June 2020, 64.

  6. CDIF, 2024: Cross Domain Interoperability Framework, https://github.com/Cross-Domain-Interoperability-Framework?ysclid=lzyh3jpr6n23169162.(last accessed in 12.08.2024).

  7. ENVIROMIS, 2024: International Conference on Environmental Observations, Modeling, and Information System. July 01–06, 2024. Tomsk, Russia, selected papers,
    227267, https://enviromis.ru/inc/files/2024/env24abs_web.pdf.

  8. ESIMO, 2024: Edinaya gosudarstvennaya sistema informatsii o sostoyanii Mirovogo oceana (Unified state system of information on the situation in the World Ocean), http://esimo.ru.(last accessed in 12.12.2024).

  9. FAIR Principles, GOFAIR, 2016: https://www.go-fair.org/fair-principles/.(last accessed in 17.06.2024).

  10. Fueling the AI transformation, 2022: Four key actions powering widespread value from AI, right now. Deloitte’s State of AI in the Enterprise, 5th Edition report. October 2022, 49.

  11. Jinfeng, Wen, Han Peng-Fei, Zhou, and Xu-Sheng Wang, 2024: Lake level dynamics exploration using deep learning, artificial neural network, and multiple linear regression techniques. https://link.springer.com//article/10.1007/s12665-019-8210-7?fromPaywallRec=false.(access date: 26.08.2024).

  12. Meiyan, Jiao, Song Lianchun, Jiang Tong, Di Zhang, Zhai Jianqing, 2015: Vypusk v Kitae zablagovremennykh preduprezhdenij s uchetom vozmozhnykh posledstvij i ocenki riskov (Impact-based and risk-based early warning in China). WMO Bulletin, 64 (2), 9–12.

  13. Meteum 2.0, 2024: https://yandex.ru/pogoda/maps/nowcast?le_Lightning=1.(last accessed in 12.08.2024).

  14. Metodicheskiye ukazaniya po podgotovke ezhegodnogo doklada “O kompleksnoy otsenke sostoyaniya natsionalnoy bezopasnosti Rossiyskoy Federatsii v oblasti morskoy deyatelnosti” (Methodological guidelines for the preparation of the annual report “On a comprehensive assessment of the state of national security of the Russian Federation in the field of marine activities”. Approved by the Marine Board under the Government of the Russian Federation. July 6, 2011, Protocol No. 2 (16), with amendments dated 26.12.2011, no. P4-54421, submitted by the Government of the Russian Federation to the President of the Russian Federation.

  15. Peresmotrennaya dorozhnaya karta dlya Desyatiletiya Organizatsii Obyedinennykh Natsiy, posvyashchennogo nauke ob okeane v interesakh ustoychivogo razvitiya. (Revised Roadmap for the United Nations Decade of Ocean Science for Sustainable Development). Updated version 2.0 dated 10.06.2018. IOC/EC-LI/2 Annex 3. UNESCO IOC. Fifty-first session of the Executive Board of UNESCO, Paris, 3–6 July 2018. Original: English.

  16. Potapov, I. I. and V. Yu. Soldatov, 2021: Iskusstvennyj intellekt: problemy i perspektivy (Artificial Intelligence: Problems and Prospects). Journal of Environmental and Natural Resources Problems, 8, 3–18.9.

  17. Stahl Bernd Carsten, 2021: Artificial Intelligence for a Better Future. An Ecosystem Perspective on the Ethics of AI and Emerging Digital Technologies. Foreword by Julian Kinderlerer. Springer. Centre for Computing and Social Responsibility De Montfort University, Leicester, UK, 124, https://doi.org/10.1007/978-3-030-69978-9.

  18. Strategy for the Development of Maritime Activities of the Russian Federation until 2030. Approved by the Order of the Government of the Russian Federation of 08.12.2010. No. 2205-r.

  19. Taesam, Lee, Singh P. Vijay, and Hwa Cho Kyung, 2021: Deep Learning for Hydrometeorology and Environmental Science. Book series: Water Science and Technology Library, 99, 204, https://link.springer.com/book/10.1007/978-3-030-64777-3.

  20. Tomorrow Company, https://www.tomorrow.io/blog/tomorrow-io-unveils-first-weather-climate-generative-ai/ (last accessed in 12.08.2024).

  21. TRUST, 2016:Core Trustworthy Data Repositories Requirements. Version: 01.00. 2016. ICSU, World Data System, 14, http://www.coretrustseal.org/wp-content/uploads/2017/01/Core_Trustworthy_Data_Repositories_Requirements._01_00.pdf (last accessed in 02.10.2024).

  22. Viazilov, E. D., S. S. Malakhov, and A. R. Askarov, 2024: Primeneniye tekhnologiy iskusstvennogo intellekta dlya podderzhki resheniy rukovoditeley predpriyatiy s ispolzovaniyem gidrometeorologicheskoy informatsii (Application of artificial intelligence technologies to support decisions of enterprise managers using hydrometeorological information). Meteorology and hydrology, 5, 87–96.

  23. Viazilov, E. D. and D. A. Melnikov, 2024: Povyshenie osvedomlennosti rukovoditelej predpriyatij morskoj deyatel’nosti dlya adaptacii k opasnym i ehkstremal’nym yavleniyam (Raising awareness among managers of marine enterprises to adapt to hazardous and extreme events). Ocean Research, 52 (2), 169–182, https://doi.org/10.29006/1564-2291.JOR-2024.52(2).9.

  24. Viazilov, E. D., D. A. Melnikov, N. V. Chunyaev, and A. E. Kobelev, 2014: Metadannyye – osnova avtomatizatsii po sozdaniyu informatsionnoy produktsii (Metadata – the basis for automation of information product creation). Infrastructure of scientific information resources and systems. Collection of selected scientific articles. Proceedings of the Fourth All-Russian Symposium (St. Petersburg, October 6–8, 2014), Ed. by E. V. Kudashev, V. A. Serebryakov, Moscow, Computing Center of the Russian Academy of Sciences, 1, 52–68.

  25. Viazilov, E. D., N. N. Mikhailov, and A. E. Kobelev, 2007: Edinaya gosudarstvennaya sistema informatsii ob obstanovke v Mirovom okeane: integratsiya informatsionnykh resursov i metadannyye (Unified state system of information on the situation in the World Ocean: integration of information resources and metadata). Fifth Anniversary Open All-Russian Conference “Remote Sensing of the Earth from Space”, Moscow, IKI RAS, November 12–16, 2007. http://d33.infospace.ru/d33_conf/2008_pdf/2/9.pdf.

  26. Viazilov, E. D., 2021: Tsifrovaya transformatsiya gidrometeorologicheskogo obespecheniya. Podkhody po realizatsii (Digital transformation of hydrometeorological support users – Implementation approaches), Obninsk, RIHMI-WDC, 1, 356; 2, 355.

  27. Viazilov, E. D., 2024: Tsifrovyye dvoyniki i tsifrovyye modeli v oblasti nauk o Zemle. (Digital twins and digital models in the field of Earth sciences), International Conference on Environmental Observations, Modeling, and Information System (ENVIROMIS’2024), July 01–06, 2024. Russia, Tomsk, Selected papers, 220–226, https://enviromis.ru/inc/files/2024/env24abs_web.pdf.

  28. Zagorecki, Adam, David Johnson, and Jozef Ristvej, 2013: Data mining and machine learning in the context of disaster and crisis management. January 2013. International J. of Emergency Management, 9 (4), 351–365, https://doi.org/10.1504/IJEM.2013.059879.

  29. Zayavka na registratsiyu bazy dannykh “Lokalnyye porogovyye znacheniya pokazateley gidrometeorologicheskikh usloviy” (Application for registration of the database “Local threshold values of hydrometeorological conditions indicators”). Rospatent, 2024.

Published
2025-06-30
Section
Geoinformatics and marine environment monitoring