{"id":2188,"date":"2025-05-13T11:46:00","date_gmt":"2025-05-13T10:46:00","guid":{"rendered":"http:\/\/newskbncran.ru\/?page_id=2188"},"modified":"2026-03-30T15:46:46","modified_gmt":"2026-03-30T14:46:46","slug":"26-5-10-en","status":"publish","type":"page","link":"https:\/\/izvestiyakbncran.ru\/index.php\/en\/26-5-10-en\/","title":{"rendered":"26.5.10 en"},"content":{"rendered":"\n

Construction of Kohonen self-organizing map (SOM) for prediction of mudflow types<\/strong><\/h4>\n\n\n\n

R.A. Zhilov<\/strong><\/strong><\/p>\n\n\n\n

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Upload the full text<\/strong><\/p>\n\n\n\n

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Abstract.<\/em><\/strong> <\/strong>The paper describes a self-organizing Kohonen map (SOM) that analyzes the mudflow type. SOM  is  trained  on  real  cadastre  data of mudflow  danger  in  the  south  of  the  European  part  of  Russia. The purpose of the work is to obtain forecasts of mudflow types. The results of the work show that SOM provides good accuracy in predicting mudflow types. The main task will be to cluster data related to geological and meteorological factors in order to identify patterns that can be used to predict the risk of  occurrence  of  various  mudflow  types.  It  is  expected  that  the  results  of  this  work  will  contribute to more accurate and on time forecasting of mudflows, which, in turn, will help minimize damage from these natural phenomena.<\/p>\n\n\n\n

Keywords:<\/em><\/strong> <\/strong>data clustering, SOM clustering method, SOM model, Kohonen self-organizing maps, mudflow type classification<\/p>\n\n\n\n

For citation<\/strong>. Zhilov R.A. Construction of Kohonen self-organizing map (SOM) for prediction of mudflow types. News of the Kabardino-Balkarian Scientific Center of RAS.<\/em>2024. Vol. 26. No. 5. Pp. 129\u2013137. DOI: 10.35330\/<\/strong>1991-6639-2024-26-5-129-137<\/p>\n\n\n\n

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References<\/strong><\/summary>\n
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  1. Khvorostov V.V., Khvorostov I.I. Extraordinary and ultra-seismic flows in the territory of the Greater Caucasus. Materialy mezhdunarodnoy konferentsii \u00abUstoychivoye razvitiye gornykh territoriy\u00bb<\/em> [Proceedings of the international conference \u201cSustainable Development of Mountain Territories\u201d]. 2004. P. 605. (In Russian)<\/li>\n\n\n\n
  2. Kondratyeva N.V., Adzhiev A.Kh., Bekkiev M.Yu. et al. Kadastr selevoy opasnosti Yuga yevropeyskoy chasti Rossii<\/em> [Inventory of mudflow danger in the south of the European part of Russia]. Moscow: Feoriya, Nalchik: Pechatnuy dvor, 2015. 148 p. (In Russian)<\/li>\n\n\n\n
  3. Kondratieva N.V. Preliminary assessment of the maximum volume of solid mudflow deposits using mathematical statistics methods for the Central Caucasus [Electronic journal]. Sovremennyye problemy nauki i obrazovaniya<\/em> [Modern problems of science and education]. 2014. No. 4. Pp. 50\u201356. (In Russian)<\/li>\n\n\n\n
  4. Kohonen T. Self-Organizing Maps (Third Extended Edition). New York, 2001. 501 p.<\/li>\n\n\n\n
  5. Zhilov R.A. Application of neural networks in data clustering. News of the KabardinoBalkarian Scientific Center of RAS<\/em>. 2021. No. 1(99). Pp. 15\u201319. DOI: 10.35330\/1991-6639-2021-1-99-15-19. (In Russian)<\/li>\n\n\n\n
  6. Radeev N.A. Prediction of avalanche danger using machine learning methods. Vestnik NGU. Seriya: Informatsionnyye tekhnologii<\/em> [NSU Bulletin. Series: Information Technologies]. 2021. Vol. 19. No. 2. Pp. 92\u2013101. DOI: 10.25205\/1818-7900-2021-19-2-92-101. (In Russian)<\/li>\n\n\n\n
  7. Flakh P. Mashinnoye obucheniye: nauka i iskusstvo postroyeniya algoritmov, kotoryye izvlekayut znaniya iz dannykh<\/em> [Machine learning: the science and art of building algorithms that extract knowledge from data]. Moscow: DMK Press. ISBN: 978-5-97060-273-7. 2015. 400 p. (In Russian)<\/li>\n<\/ol>\n\n\n\n

    <\/p>\n<\/details>\n\n\n\n

    Information about the author<\/strong><\/summary>\n

    Ruslan A. Zhilov<\/strong>, Junior Researcher, Neuroinformatics and Machine Learning Department, Institute of Applied Mathematics and Automation \u2013 branch of Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences;
    360000, Russia, Nalchik, 89 A Shortanov street;
    zhilov91@gmail.com, ORCID: https:\/\/orcid.org\/0000-0002-3552-4854, SPIN-\u043a\u043e\u0434: 9389-6188<\/p>\n\n\n\n

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    Construction of Kohonen self-organizing map (SOM) for prediction of mudflow types R.A. Zhilov Upload the full text Abstract. The paper describes a self-organizing Kohonen map (SOM) that analyzes the mudflow type. SOM  is  trained  on  real  cadastre  data of mudflow  danger  in  the  south  of  the  European  part  of  Russia. The purpose of the work […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-2188","page","type-page","status-publish","hentry"],"yoast_head":"\n26.5.10 en - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/izvestiyakbncran.ru\/index.php\/en\/26-5-10-en\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"26.5.10 en - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb\" \/>\n<meta property=\"og:description\" content=\"Construction of Kohonen self-organizing map (SOM) for prediction of mudflow types R.A. 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