proanarisПроблемы анализа рискаIssues of Risk Analysis1812-52202658-7882ФГБУ ВНИИ ГОЧС (ФЦ)proanaris-824Research ArticleРИСК ПРИРОДНЫЙNATURAL RISKМодель определения вероятности возникновения затора в створе рекиModel for Determining the Probability of Congestion in a River Formationhttps://orcid.org/0009-0003-2559-3391ТкаченкоЮ. А.TkachenkoYu. A.

Ткаченко Юлия Анатольевна - младший научный сотрудник научно-исследовательского отдела (по проблемам гражданской обороны и  чрезвычайным ситуациям) научно-исследовательского центра 

Количество публикаций: 19Область научных интересов: безопасность в чрезвычайных ситуациях, защита населения и территорий от чрезвычайных ситуаций.141435, Московская область, г.  Химки, мкр. Новогорск, ул. Соколовская, стр. 1А

Yuliya A. Tkachenko  

Sokolovskaya str., bld. 1А, Moscow region, Khimki, md. Novogorsk, 141435

yu.tkachenko@agz.50.mchs.gov.ruФГБВОУ ВО «Академия гражданской защиты МЧС России»РоссияCivil Defence Academy of Emercom of RussiaRussian Federation2024210620242134249Copyright © Ткаченко Ю.А., 20242024Ткаченко Ю.А.Tkachenko Y.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.risk-journal.com/jour/article/view/824

Имеющиеся сведения о значениях факторов образования затора в створе реки дают возможность оценить вероятность его возникновения. Регрессионный анализ вероятности и значений факторов позволяет построить зависимость между этими величинами, однако выполнить его привычными методами статистической обработки достаточно проблематично ввиду того, что зависимая переменная является дискретной, бинарной. В статье представлен подход к оценке вероятности возникновения затора с точки зрения классификации фактов возникновения затора и его отсутствия в зависимости от значений факторов образования заторов. В результате получена модель определения вероятности возникновения затора в створе реки, в основе которой находится метод логистического преобразования.

The available information on the values of the factors of congestion formation in the river bed makes it possible to assess the probability of its occurrence. Regression analysis of probability and factor values allows you to build a relationship between these values, but it is quite problematic to perform it using the usual methods of statistical processing due to the fact that the dependent variable is discrete, binary. The article presents an approach to assessing the likelihood of congestion from the point of view of classifying the facts of congestion and its absence depending on the values of the factors of congestion formation. As a result, a model was obtained for determining the probability of congestion in the riverbed, which is based on the method of logistic transformation.

бинарная логистическая регрессиязаторнаводнениеинженерно-технические мероприятиязащита населения и территорий от наводненийbinary logistic regressioncongestionfloodingengineering and technical measuresprotection of the population and territories from floods.ReferencesTkachenko Yu.A. Analysis of the hydrometeorological factors of formation of conflicts on the rivers of the Tomsk region / Yu.A. Tkachenko, A.N. Tedurieva, E.E. Ostapchuk // Improving the Unified State System of Emergency Prevention and Response and Civil Defense of the Russian Federation at the present stage: Proceedings of Section No. 1 of the XXXIII International Scientific and Practical Conference, Khimki, March 01, 2022. Khimki: Academy of Civil Protection of the Ministry of the Russian Federation for Civil Defense, Emergencies and Elimination of Consequences of Natural Disasters, 2022. P. 101–105. (In Russ.)Tkachenko Yu.A. Analysis of the hydrometeorological factors of formation of conflicts on the rivers of the Tomsk region / Yu.A. Tkachenko, A.N. Tedurieva, E.E. Ostapchuk // Improving the Unified State System of Emergency Prevention and Response and Civil Defense of the Russian Federation at the present stage: Proceedings of Section No. 1 of the XXXIII International Scientific and Practical Conference, Khimki, March 01, 2022. Khimki: Academy of Civil Protection of the Ministry of the Russian Federation for Civil Defense, Emergencies and Elimination of Consequences of Natural Disasters, 2022. P. 101–105. (In Russ.)Kozlov D.V., Kuleshov S.L. Multidimensional data analysis of jamming factors in river basins // Vodnye resursy. 2019;46(2):132–141. (In Russ.). https://doi.org/10.31857/S0321–0596462132–141]Kozlov D.V., Kuleshov S.L. Multidimensional data analysis of jamming factors in river basins // Vodnye resursy. 2019;46(2):132–141. (In Russ.). https://doi.org/10.31857/S0321–0596462132–141]Agafonova S.A., Berkovich K.M., Ruleva S.N. [et al.] Present-day specific features of the channel morphology and ice-jam formation at the Tom river // Water Sector of Russia: Problems, Technologies, Management. 2012;(6):21–33. (In Russ.)Agafonova S.A., Berkovich K.M., Ruleva S.N. [et al.] Present-day specific features of the channel morphology and ice-jam formation at the Tom river // Water Sector of Russia: Problems, Technologies, Management. 2012;(6):21–33. (In Russ.)Tkachenko Yu.A., Tkachenko P.N., Tedurieva A.N., Ivanov E.V. Analysis of scientific and methodological approaches to prediction of jam phenomena in the Tomsk region // Scientific and Educational Tasks of Civil Defence. 2022;(2):29–37. (In Russ.)Tkachenko Yu.A., Tkachenko P.N., Tedurieva A.N., Ivanov E.V. Analysis of scientific and methodological approaches to prediction of jam phenomena in the Tomsk region // Scientific and Educational Tasks of Civil Defence. 2022;(2):29–37. (In Russ.)Tkachenko Yu.A., Tkachenko P.N., Ivanov E.V. On the approach to solving the problem of reducing the likelihood of congestion on the river section // Issues of Risk Analysis. 2024;21(1):56–64. (In Russ.)Tkachenko Yu.A., Tkachenko P.N., Ivanov E.V. On the approach to solving the problem of reducing the likelihood of congestion on the river section // Issues of Risk Analysis. 2024;21(1):56–64. (In Russ.)Pampel F., Gruzdev A., Tsvirkun D. Logistic regression. M.: DMK Press, 2023. 218 p. (In Russ.)Pampel F., Gruzdev A., Tsvirkun D. Logistic regression. M.: DMK Press, 2023. 218 p. (In Russ.)Bogdanov L.Y. The evaluation of performance of binary classifiers based on logistic regression using ROC analysis // Bulletin of the Saratov State Technical University. 2010;4(2):92–97. (In Russ.)Bogdanov L.Y. The evaluation of performance of binary classifiers based on logistic regression using ROC analysis // Bulletin of the Saratov State Technical University. 2010;4(2):92–97. (In Russ.)

The authors declare that there are no conflicts of interest present.