СOMPUTER MODELING PARAMETERS OF TECHNOGENIC EMERGENCY SITUATIONS ON ENGINEERING INFRASTRUCTURE OF THE MEGAPOLIS

Authors

  • M. V. Novozhylova O. M. Beketov National University of Urban Economy in Kharkiv, Kharkiv, Ukraine.
  • V. A. Andronov National University of Civil Defense of Ukraine
  • R. S. Melezhik National University of Civil Defense of Ukraine, Kharkiv, Ukraine.

DOI:

https://doi.org/10.15588/1607-3274-2021-1-7

Keywords:

space-time series, nonstationary Poisson distribution, simulation model, engineering infrastructurе.

Abstract

Context. The urgency of the research is to develop methods for analyzing and processing space-time information, namely the set of data distributed both in space and time and creating on this basis a computer probabilistic model of the process of predicting manmade emergencies on city engineering infrastructure. The spatio-temporal nature of data series causes additional requirements for the identification procedures of the mathematical model of a series, therefore, the number of approaches identifying its structure and construction of a series model has been proposed.

Objective is methodical and software implementation of a computer model of the space-time series being intended to predict the future values of locations and times of man-made emergencies on the engineering infrastructure of the metropolis and increase decision-making efficiency.

Method. A projection approach providing independent determination of random spatial parameters defining location of emergency units on engineering infrastructure as a sequence of two one-dimensional uniform distributions and describing time distribution of moments of accidents as non-stationary Poisson distribution has been developed. Proposed is an integrated approach which includes the construction of generator points, the power of which (characteristic of the accident complexity) based on the implementation of the comparative statics approach with so-called cumulative effect within a certain time. A relaxation approach based on the reduction of a two-dimensional simulation model of determining the city of possible emergency location to a set of independent onedimensional non-stationary (including stationary) distributions to generate the time of occurrence has been constructed. Formalization of the space-time field, procedures of information support of the process of forecasting the parameters of a possible emergency, typification of initial data for numerical experiments on the implementation of methods for forecasting the parameters of a possible emergency on the example of water supply and sewerage network of utility company Kharkivvodokal, city Kharkiv have been developed.

Results. A dual methodology to determine the simulation model parameters of the space-time series, which contains both projection and integral approaches, as well as a combined method − relaxation approach, have been proposed. Numerical experiments based on the constructed model were performed. The model being considered is the theoretical basis to construct the forecast using a large amount of historical data.

Conclusions. The method to predict the parameters of space-time series considering the nonstationarity property of the time component distribution has been further developed. Using the proposed computer simulation tools allows to increase the accuracy of the forecast of the location, time of occurrence and severity of a possible accident on the engineering infrastructure of the metropolis. 

Author Biographies

M. V. Novozhylova , O. M. Beketov National University of Urban Economy in Kharkiv, Kharkiv, Ukraine.

Dr. Sc., Professor, Head of the Department of Computer Science and Information Technologies. 

V. A. Andronov , National University of Civil Defense of Ukraine

Dr. Sc., Professor, Vice-Rector.

R. S. Melezhik , National University of Civil Defense of Ukraine, Kharkiv, Ukraine.

Adjunct.  

References

Ye J., Moreno-Madriñán M. J. Comparing different spatiotemporal modeling methods in dengue fever data analysis in Colombia during 2012–2015, Spatial and Spatio-temporal Epidemiology, 2020, Vol. 34, 100360. DOI: /10.1016/j.sste.2020.100360.

Medrano R., Aznarte J. L. A spatio-temporal attention-based spot-forecasting framework for urban traffic prediction, Applied Soft Computing, 2020, Vol. 96, 106615. DOI:10.1016/j.asoc.2020.106615.

Chen P., Xie R., Lu M., Huang Z. The impact of the spatiotemporal neighborhood effect on urban eco-efficiency in China, Journal of Cleaner Production, In Press. Available online 30 October 2020. DOI:10.1016/j.jclepro.2020.124860.

Murakami D., Yamagata Y., Hirano T. Geostatistics and Gaussian process models, Spatial Analysis Using Big Data, Academic Press, 2020, P.57–112. DOI:10.1016/b978-0-12813127-5.00004-7.

Kyriakidis P., Journel C. A. Geostatistical space-time models: a review, Mathematical Geology, 1999, Vol. 31, pp. 651–684.

Rashid T. Make Your Own Neural Network, Create Space Independent Publishing Platform, 2016, 222 p.

Subbotin S. O., Olijnyk A. O., Olijnyk O. O. Neiteratyvni, evoljucijni ta mul’tyagentni metody syntezu nechitkologichnyh i nejromerezhnyh modelej, Monografija, Zaporizhzhja, ZNTU, 2009, 375 p.

Dudin A.N., Nishimura S. Optimal hysteresis control for a BMAP/SM/1/N queue with two operation modes. Mathematical Problems in the Engineering, 2000, No. 5, pp. 397– 420.

Schller J. C. H., Brinkman L., Van Gestel P. J., Van Otterloo R. W. Methods for determining and processing probabilities «Red Book». The Netherlands, Committee for Prevention of Disasters, 1997, 604 р.

Tvoroshenko I. S., Kramarenko O. O. Software determination of the optimal route by geoinformation technologies, Radio Electronics, Computer Science, Control, 2019, No. 5, рр. 131–142. DOI:10.15588/1607-3274-2019-3-15

Ongkowijoyo C. S., Doloi H. Risk-based resilience assessment model focusing on urban infrastructure system restoration, Procedia Engineering, 2018, Vol. 212, pp. 1115–1122. DOI:10.1016/j.proeng.2018.01.144

Popov V. M., Chub I. A., Novozhilova M. V. Modelirovanie harakteristik potoka otkazov osnovnyh proizvodstvennyh fondov ob#ektov povyshennoj opasnosti, Problemi nadzvichajnih situacіj, 2015, Vol. 21, pp. 93–98.

Komarnyc’kyj I. M., Bublyk M. I. Ocinka tehnogennyh zbytkiv ta analiz pidhodiv do i’hn’ogo rozrahunku u global’nomu ta regional’nomu aspektah. [Online]. 2008, Available: vlp.com.ua/files/21_31.pdf.

Ishhenko G. G. Analiz ta prognoz pryrodno-tehnogennoi’ bezpeky velykyh mist iz zastosuvannjam nelinijnyh metodiv. Ekonomika i derzhava, 2009, № 8, pp. 48–53.

He Y., Du S. Classification of urban emergency based on fuzzy analytic hierarchy process, Procedia Engineering, 2016, Vol. 137., pp. 630–638.

Gubarevych O. V. Nadijnist’ i diagnostyka elektroobladnannja: Pidruchnyk. Sjevjerodonec’k: vyd-vo SNU im. V. Dalja, 2016, 248 p.

Pro zatverdzhennia Klasyfikatsiinykh oznak nadzvychainykh sytuatsii: Nakaz Ministerstva vnutrishnikh sprav Ukrainy vid 06.08.2018r. № 658. [Online]. Available: https://zakon.rada.gov.ua/laws/show/z0969-18#Text

Published

2021-03-26

How to Cite

Novozhylova , M. ., Andronov , V. A. ., & Melezhik , R. S. . (2021). СOMPUTER MODELING PARAMETERS OF TECHNOGENIC EMERGENCY SITUATIONS ON ENGINEERING INFRASTRUCTURE OF THE MEGAPOLIS . Radio Electronics, Computer Science, Control, 1(1), 66–77. https://doi.org/10.15588/1607-3274-2021-1-7

Issue

Section

Mathematical and computer modelling