FORMALIZED MATHEMATICAL MODEL FOR FORECASTING EMERGENCY SITUATIONS AND POSSIBLE DAMAGE CAUSED BY THEM
DOI:
https://doi.org/10.26906/SUNZ.2020.4.092Keywords:
ormalized mathematical model, control algorithm, emergency, nature, type, level of emergency, losses from emergenciesAbstract
Relevance. Prevention of emergencies is based on analysis, forecasting of threats of emergencies and their possible consequences both in the state and in its regions. At the same time, forecasting emergencies should be aimed at regulating manmade, natural and social security in the country, assessing the threat of emergencies and their possible consequences. Objective. Development of a formalized mathematical model for predicting emergencies and possible losses because of them. Method. A systematic approach to solving the problem of predicting emergency situations and possible losses caused as a result of them both in the state and its regions in order to prevent their occurrence or minimize possible consequences involves forecasting emergency situations in the country as a whole and its regions; forecasting natural emergencies in general, by types and levels in the state; forecasting emergency situations of a technogenic nature; forecasting emergency situations of a social nature by type and level and forecasting losses due to emergency situations in the state. Results. A formalized mathematical model for forecasting emergencies, possible losses caused because of them and a control algorithm that implements the developed mathematical model has been developed. Conclusions. A formalized mathematical model for predicting emergencies and possible losses caused as a result of them includes mathematical models for predicting the process of emergencies in the state; forecasting the process of emergencies in the regions of the state, forecasting emergencies by nature, types and levels both in the state as a whole and in its regions; forecasting possible losses as a result of emergency situations. The obtained results of the study are the foundation for substantiating organizational and technical measures for responding to emergencies in order to prevent them and minimize possible consequences
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Звіт про основні результати діяльності Державної служби України з надзвичайних ситуацій у 2018 році. [Електрон.ресурс]. – Режим доступу: URL: http://www.dsns.gov.ua/files/2018/1/26/Zvit%202018(КМ).pdf.
Звіт про основні результати діяльності Державної служби України з надзвичайних ситуацій у 2017 році. URL: http://www.dsns.gov.ua/ files/2018/1/26/Zvit%202017(КМУ). pdf (дата доступу: 08.08.2018).
Tiutiunyk V.V. Ivanets H.V. Tolkunov I.A. Stetsyuk E.I. (2018). System approach for readiness assessment units of civil defense to actions at emergency situations. Scientific Bulletin of National Mining University. Vol. 1. Is. 1 (163). Pp. 99-105.
Голован Ю.В., Козырь Т.В. Защита населения в чрезвычайных ситуациях. Организационно-методический комплекс. Издательство «Проспект», Дальневосточный государственный технический университет. 2015. 219 с.
Nivolianitou Z., Synodinou B. (2011). A Towards emergency management of natural disasters and critical accidents: The Greek experience. Journal of Environmental Management.. Vol. 92. Issue. 10. pp. 2657-2665. Doi: https// doi: org/ 10.1016/j.jenvman.2011.06.003.
Новоселов С.В., Панихидников С.А. Проблемы прогнозирования количества чрезвычайных ситуаций статистическими методами. Горный информационно-аналитический бюллетень. 2017. No10. С. 60-71.
Ivanets H., Horielyshev S., Ivanets M., D. Baulin, Tolkunov I., Gleizer N., Nakonechnyi A. (2018). Development of combined method for predicting the process of the occurrence of emergencies of natural character. Eastern-European Journal of Enterprise Technologies. Vol. 5. Issue 10(95). pp. 48-55. doi:https://doi.org/10.15587/1729-4061.2018.143045.
Баскин Ю.Г., Горбунов А.А., Савельев Ю.С. Предупреждение чрезвычайных ситуаций природного и техногенного характера. Проблемы управления рисками в техносфере. СП6 No3. СП6 УГПС МЧС России, 2014.
Deng, S.C., Wu, Q., Shi, B., et al. (2014) Prediction of Resource for Responding Waterway Transportation Emergency Based on Case-Based Reasoning. China Safety Science Journal Vol. 24. pp 79-84.
Tsurikov A.N. (2013). Application of artificial neural network for identification of stability of botton layer of atmosphere. Applied and Fundamental Studies: Proceedings of the 2nd International Academic Conference. Publishing House «Science and Innovation Center». St. Louis, Missouri, USA. pp. 226-231.
Vasiliev M., Movchan I., Koval O. (2014). Diminishing of ecological risk via optimization of fire-extinguishing system projects in timber-yards. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. Issue 5. pp. 106–113.
Mygalenko, K., Nuyanzin, V., Zemlianskyi, A., Dominik, A., Pozdieiev, S. (2018). Development of the technique for restricting the propagation of fire in natural peat ecosystems. Eastern-European Journal of Enterprise Technologies.Vol. 1 (10(91)). Pp. 31–37. doi: 10.15587/1729-4061.2018.121727.
Pradhan R.P., Kumar R. (2010). Forecasting Exchange Rate in India: An Application of Artificial Neural Network Model.Journal of Mathematics Research. Vol. 2. No.4. pp. 111-117.
Sun, B.Z., Ma, W.M. and Zhao, H.Y. (2013). A Fuzzy Rough Set Approach to Emergency Material Demand Prediction overTwo Universes. Applied Mathematical Modeling Vol. 37. pp. 7062-7070. http://dx.doi.org/10.1016/j.apm.2013.02.008.
Al-Jumeily D., Ghazali R. (2014). Hussain A.Predicting physical time series using dynamic ridge polynomial neural networks. PLoS ONE. No9: e105766. doi: 10.1371/journal.pone.0105766.
Ямалов И.У. Моделирование процессов управления и принятия решений в условиях чрезвычайных ситуаций. Москва, БИНОМ. Лаборатория знаний. 2015. 291 с.
