Description: Prevention origin extraordinary situations is a complex legal, socio-economic, political, organizationally technical and other measures, directed on adjusting natural and technogenic safety, leadthrough estimation risk levels, done early reacting, on the threat origin extraordinary situations on the basis information of monitoring, examination, researches and prognoses, in relation to the chapter possibilities with the purpose non-admission their outgrowing in extraordinary situations or softening of them possible consequences. An important aspect in relation to prevention and warning origin extraordinary situations is timely prog-nostication process their origin with the purpose minimization consequences from them. For prognostication process origin extraordinary situations the methods of regressive analysis are widely utilized in the state. The regressive model such process, as a rule, carries nonlinear character and appears as a sedate polynomial. At the estimation model parameters a least-squares method not always is provide constancy dispersion tailings for every supervision or group of supervisions. It results in that the parameters regressive model will not have minimum dispersion, that worsens exactness of prognosis. In the article the algorithm of prognostication process origin extraordinary situations is offered taking into account the errors of regressive model and clari-fication estimations its parameters on the basis the self-weighted least-squares method. The results experimental researches con-firm efficiency of application the self-weighted least-squares method for the increase exactness prognostication process origin of extraordinary situations at the use regressive models. Can be drawn on the got results for the ground of organizationally-technical measures in relation to providing readiness subdivisions and formings of civil defence, in particular Government service on the extraordinary situations of Ukraine, for the adequate reacting or warning of extraordinary situations and minimization their pos-sible consequences.
Keywords: extraordinary situation, regressive model, self-weighted least-squares method, exactness of prognosis
1. State Service of Ukraine for Emergency Situations (2018), “Zvit pro osnovni rezultaty diyalnosti Derzhavnoi sluzhby Ukrainy z nadzvychainykh sytuatsiy u 2017 rotsi” [Report on the main results of the State Service of Ukraine for Emergencies in 2017], available at: www.dsns. gov.ua/files/2018/1/26/Zvit%202017(КМУ).pdf.
2. Guskova, N.D. and Neretina, E.A. (2013), Threats of natural character, factors affecting sustainable development of territories and their prevention, Journal of the Geographical Institute Jovan Cvijic, Vol. 63, Issue 3, pp. 227-237. https://doi.org//10.2298/ijgil303227g.
3. Tiutiunyk, V.V., Ivanetz, H.V., Tolkunov, I.A. and 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, pp. 99-105. https://doi.org/10.29202/nvngu/2018-1/7.
4. Ivanets, H., Horielyshev, S., Ivanets, M., Baulin, D., Tolkunov, I., Gleizer, N. and 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. https://doi.org/10.15587/1729-4061.2018.143045.
5. Golovan, Yu.V. and Kozyr', T.V. (2015), “Zashchita naseleniya v chrezvychaynykh situatsiyakh. Organizatsionno-metodicheskiy kompleks” [Defence of population is in emergency situations. Organizationally-methodical complex], Dal'nevostochnyy gosudarstvennyy tekhnicheskiy universitet, 219 p.
6. Ivanets, H.V. (2016), “Analiz stanu tekhnohennoi, pryrodnoi ta sotsialnoi nebezpeky administratyvno-terytorialnykh odynyts Ukrainy na osnovi danykh monitorynhu” [Analysis of technogenic, natural and social danger the administrative-territorial units of Ukraine on the basis of monitoring data], Scientific Works of Kharkiv National Air Force University, Vol. 3(48), pp. 142-145.
7. Neisser, F. and Runkel, S. (2017), The future is now! Extrapolated riskscapes, anticipatory action and the management of potential emergencies, Geoforum, Vol. 82, pp. 170-179. https://doi.org/10.1016/j.geoforum.2017.04.008.
8. Kryanev, A., Ivanov, V., Romanova, A., Sevastianov, L. and Udumyan, D. (2018), Extrapolation of Functions of Many Variables by Means of Metric Analysis, EPJ Web of Conferences, Vol. 173:03014. https://doi.org/10.1051/epjconf/201817303014.
9. Migalenko, K., Nuianzin, V., Zemlianskyi, A., Dominik, A. and 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, Issue 10(90), pp. 31-37. https://doi.org//10.15587/1729–4061.2018.121727.
10. Junk, C., Delle Monache, L., Alessandrini, S., Cervone, G. and von Bremen, L. (2015), Predictor-weighting strategies for probabilistic wind power forecasting with an analog ensemble, Meteorologische Zeitschrift, Vol. 24, Issue 4, pp. 361-379. https://doi.org/10.1127/metz/2015/0659.
11. Morariu, N., Iancu, E. and Vlad, S. (2009), A neural network model for time series forecasting, Romanian Journal of Economic Forecasting, Issue 4, pp. 213-223.
12. Pradhan, R.P. and Kumar, R. (2010), Forecasting Exchange Rate in India: An Application of Artificial Neural Network Model, Journal of Mathematics Research, Vol. 2, Issue 4, pp. 111-117. https://doi.org/10.5539/jmr.v2n4p111.
13. Al-Jumeily, D., Ghazali, R. and Hussain, A. (2014), Predicting Physical Time Series Using Dynamic Ridge Polynomial Neural Networks, PLoS ONE, Vol 9. Issue 8, pp. e105766. https://doi.org/10.1371/journal.pone.0105766.
14. Szoplik, J. (2015), Forecasting of natural gas consumption with artificial neural networks, Energy, Vol. 85, pp. 208-220. https:// doi.org/10.1016/j.energy.2015.03.084.
15. Balasyanyan, S.Sh. and Gevorgyan, E.M. (2016) “Sravnitel’niy analiz metodov gruppovogo ucheta argumentov pri modelirovanii processov pererabotki poleznyh iskopaemyh” [Comparative analysis of methods group account arguments at the imagineering processes waste-handling of minerals], Izvestiya Tomskogo politekhnicheskogo universiteta. Inzhiring georesursov, Vol. 327, Issue 4, pp. 23-34.
16. Nivolianitou, Z. and Synodinou, B.A. (2011), Towards emergency management of natural disasters and critical accidents: The Greek experience, Journal of Environmental Management, Vol. 92, Issue 10, pp. 2657-2665. https://doi.org/10.1016/j.jenvman.2011.06.003.
17. Novoselov, S.V. and Panikhidnikov, S.A. (2017), “Problemy prognozirovaniya kolichestva chrezvychaynykh situatsiy statisticheskimi metodami” [Problems of prognostication amount emergency situations by statistical methods], Gornyy informatsionno-analiticheskiy byulleten', Vol. 10, pp. 60-71.
18. The Ukrainian Civil Protection Research Institute (2014),)“Natsionalna dopovid pro stan tekhnohennoi ta pryrodnoi bezpeky v Ukraini u 2013 rotsi” [National report on the state of technological and natural security in Ukraine in 2014], Kyiv, 542 p.