Russian Federation
Russian Federation
employee
Russian Federation
An analysis of modern databases on registered natural and man-made emergency situations shows their rapid increase, despite the complex of technical and organizational measures taken. The work presents the main factors contributing to the development of emergency situations The current issue of the need to improve the early warning system for natural disasters is considered. The authors of the work presented an original computer program that implements the operation of artificial neural networks capable of predicting the likelihood of the occurrence of hazardous natural meteorological phenomena. The results obtained showed satisfactory accuracy, as evidenced by regression analysis. The practical significance of the study lies in the ability to convert observation data into practical actions to prevent emergency situations of various types.
emergency, artificial neural networks, forecasting, data, public protection
1. Kolichestvo chrezvychajnyh situacij. URL: https://lifehack365.ru/kolichestvo/chs/ (data obrashcheniya: 24.06.2024).
2. Weary of many disasters? UN says worse to come. URL: https://apnews.com/article/climate-science-united-nations-natural-disasters-fa1d16 ad7d59c7629bb1a9a955a494b0 (data obrashcheniya: 24.06.2024).
3. Uchenye: poslednie sem' let stali samymi zharkimi za vsyu istoriyu nablyudenij. URL: https://daily.afisha.ru/news/ 58733-uchenye-poslednie-sem-let-stali-samymi-zharkimi-za-vsyu-istoriyu-nablyudeniy/ ( data obrashcheniya: 24.06.2024).
4. Andrunyak I.V. Ocenka veroyatnogo ushcherba ot navodnenij na osnove monitoringa i prognozirovaniya po enisejskomu bassejnovomu okrugu // Monitoring. Nauka i tekhnologii. 2023. № 1 (55). S. 58–66.
5. Metodicheskij podhod k sostavleniyu klassifikatora vyzovov obshchestvennomu zdorov'yu / T.P. Vasil'eva [i dr.] // Zdorov'e naseleniya i sreda obitaniya – ZNiSO. 2024. T. 32. № 2. S. 7–17.
6. Analiz pokazatelej pavodkoopasnoj obstanovki na territorii voronezhskoj oblasti za period s 2013 po 2023 god / D.S. Korolev [i dr.] // Tekhnosfernaya bezopasnost'. 2023. № 2 (39). S. 112–122.
7. Statisticheskij analiz chrezvychajnyh situacij prirodnogo haraktera v mire i na territorii Rossijskoj Federacii / D.S. Korolev [i dr.] // Tekhnosfernaya bezopasnost'. 2023. № 3 (40). S. 131–138.
8. Petrosyan O.H., Jzmechyan A.E. Research on the principles of artificial neural network construction and parameter modeling // Proceedings of National Polytechnic University of Armenia. Information Technologies, Electronics, Radio Engineering. 2023. № 1. S. 70–77.
9. Upravlenie sistemoj obespecheniya bezopasnosti informacionno-telekommunikacionnoj set'yu na osnove algoritmov funkcionirovaniya iskusstvennoj nejronnoj seti / M.A. Kocynyak [i dr.] // Izvestiya Tul'skogo gosudarstvennogo universiteta. Tekhnicheskie nauki. 2020. № 4. S. 3–10.
10. Gejdarov P.Sh.O. Issledovanie ustojchivosti mnogoslojnogo perseptrona s vychislyaemymi vesami sinapsov k men'shim ob"emam obuchayushchej vyborki // Informacionno-upravlyayushchie sistemy. 2023. № 2 (123). S. 2–14.
11. Mingaliev Z.Z., Kychkin I.M. Reshenie obratnyh mnogomernyh zadach na osnove mnogoslojnogo perseptrona // Vestnik sovremennyh issledovanij. 2019. № 3.3 (30). S. 30–34.
12. Butyrskij E.Yu., Matveev A.V. Matematicheskoe modelirovanie sistem i processov. SPb.: Informacionnyj izdatel'skij uchebno-nauchnyj centr «Strategiya budushchego», 2022. 733 s. ISBN 978-5-4268-0064-9. DOI:https://doi.org/10.37468/book_011222. EDN CCRIRT.
13. Programma dlya analiza dannyh informacionnyh potokov s posleduyushchej analiticheskoj i prognoznoj obrabotkoj: svidetel'stvo o gosudarstvennoj registracii programmy dlya EVM № 2024615854 Ros. Federaciya / D.S. Korolev, A.V. Vytovtov, P.S. Kuprienko, E.A. Sushko, V.I. Fedyann, A.N. Koshel', N.V. Il'ina; pravoobladatel' FGBOU VO Voronezhskij gosudarstvennyj tekhnicheskij universitet (FGBOU VO VGTU), zayavleno ot 05.03.2024 № 2024614716, gosudarstvennaya registraciya v reestre 13.03.2024.
14. Korolev D.S., Kalach A.V., Sorokina Yu.N. Sravnitel'nyj analiz sposobov prognozirovaniya fiziko-himicheskih svojstv veshchestv // Vestnik Komandno-inzhenernogo instituta MCHS Respubliki Belarus'. 2016. № 1 (23). S. 78–84.
15. Matveev A.V. Metody modelirovaniya i prognozirovaniya. SPb.: S.-Peterb. un-t GPS MCHS Rossii, 2022. 230 s. ISBN 978-5-907116-73-3. EDN IMLKWS.