DOI: https://doi.org/10.15588/1607-3274-2017-2-4

### THE MODEL OF TERITORIAL SYSTEM IN NATURAL DISASTER CONDITIONS

#### Abstract

Context. The spatial model of territorial system in natural emergency conditions dedicated to decision support tasks solving and represented as the model of complex dynamic system is described in the paper. The literature data analysis has shown that applying the established approaches to the class of complex dynamic systems under consideration doesn’t provide the required calculation speed andefficiency of decision support system. This determines the timeliness of the further searching of nontraditional models and methods of decision support, which provide the fulfillment of calculation speed and efficiency requirements applicable to such systems.

Objective. The goal of research is decreasing the damage from the natural emergency by means of improving the quality and timeless of forecasting the territorial system dynamics in the natural emergency conditions

Method. The concept of territorial system in natural emergency conditions is represented in the form of overlaying static and dynamic topological spaces induced by indiscernibility relation, each of which allows representing geographical and attributive information about nature conditions, value objects demanding protection against natural emergency, as well as about natural emergency dynamics. The model of natural emergency dynamics is based on the discretization of the space by the grid of square cells of equal area and represented in the form of fuzzy dynamic topological space in the set of cells.

Results. The web-oriented decision support system is created on the base of developed concept and model. The experiments have been conducted, which have shown that the proposed natural emergency model can provide reasonable characteristics in terms of accuracy and speed providing that the space is discretized with the size of cell being from 8 m to 18 m.

Conclusions. The concept of territorial system representation in the natural emergency conditions is first developed in the form of overlaying static and dynamic topological spaces. The formal model of the natural emergency dynamics in the form of moving the bounding region of fuzzy-rough set, which has allowed reducing computational complexity and provided adaptation to the conditions of incomplete and inaccurate information is first created. The software for realization of suggested concept and method is developed. The software has allowed performing the practical task of decision support in natural emergency conditions.

#### Keywords

#### Full Text:

PDF (Українська)#### References

Grab M. V. Modeli, metody i algoritmy rasprostranenija lesnyh pozharov: Diss. kand. tehn. nauk: 01.05.02. Har’kov, HNURJe, 2004, 230 p.

Martinez J., Vega-Garcia C., Chuvieco E. Human-caused wildfire risk rating for prevention planning in Spain, Journal of Environmental Management, 2009, No. 90, pp. 1241–1252.

Atkinson D., Chladil M., Janssen V. et al.Implementation of quantitative bushfire risk analysis in a GIS environment, International Journal of Wildland Fire, 2010, No. 19, pp. 649–658.

Chuvieco E. et al. Development of a framework for fire risk assessment usong remote sensing and geographic information system technologies, Ecological Modelling, 2010, No. 221, pp. 46–58.

Preisler H. K., Brillinger D. R., Burgan R. E. et al. Probability based models for estimating wildfire risk, International Journal of Wildland Fire, 2004, No. 13, pp. 133–142.

Genton M. G., Butry D. T., Prestemon M. L. et al. Spatiotemporal analysis of wildfire ignitions in the St Johns River Water Management District, Florida, International Journal of Wildland Fire, 2006, No. 15, pp. 87–97.

Baranovskiy N., Zharikova M. A web-oriented geoinformation system for forest fire danger prediction in typical forests of the Ukraine, Thematic cartography for the society. Lecture notes in geoinformation and cartography, Springer, 2014, pp. 13–22

Zharikova M., Sherstjuk V. Threat assessment method for ntelligent disaster decision support system, Advances in Int. Systems and Computing. Springer, 2016, Vol. 512, pp. 81–99.

Zharikova М. V., Sherstjuk V. G. Razrabotka modeli chrezvychajnoj situacii prirodnogo haraktera v sisteme podderzhki prinjatija reshenij, Vostochno-evropejskij zhurnal peredovyh tehnologij, 2015, No. 1/4(73), pp. 62–74.

Ghisy T., Arca B., Pellizaro G. et al. An improved cellular automata for wildfire spread, Procedia Computer Science: ICCS 2015 International Conference On Computational Science, Reykjavik, Iceland, 1–3 June 2015 : proceedings. Elsevier, Volume 51, 2015, pp. 2287–2296.

Anderson D. H., Catchpole E., De Meste N. J. et al. Modeling the spread of grass fires, The J. Of the Australian Mathematical Society Series B Applied Mathematics, 1082, No. 23(4), pp. 451–466.

Finney M. A. Fire area simulator-model development and evaluation: technical report : RMRS-RP-4. USDA, UT, Ogden, 2004, 50 p.

Ghisu T., Arca B., Pellizzaro G. et al. A level-set algorithm for simulating wildfire spread, CMES Computer Modeling in Engineering & Sciences, 2014, No. 102(1), pp. 83–102.

Trufino G. A., Ambrosio D. D.’, Rongo R. et al. A new algorithm for simulating wildfire through cellular automata, ACM Transactions on Modeling and Computer Simulation, 2011, No. 22(1), pp. 1–26.

Zharikova M., Sherstjik V., Baranovskiy N. The plausible wildfire model in geoinformation decision support system for wildfire response, Water resources, Forest, marine and ocean ecosystems: 15th international conference SGEM, 18–24 June 2015: proceedings. Albena, Bulgaria, 2015, pp. 575–583. DOI: 10.5593/ sgem2015B32,

Zharikova M., Sherstjik V. Development of the model of natural emergencies in decision support system, EasternEuropean Journal of Enterprise Technologies, 2015, Vol 1, No. 4(73), pp. 62–69.

Slowinski R. Salvatore G., Benedetto M. Rough set and rule-based multicriteria decision aiding, Pesquisa operacional, 2012, Vol. 32(2), pp. 213–269.

Pawlak Z. Rough Sets, Jerzy W., Slowinski R. et al., Comm. of ACM, 1995, Vol. 38, No. 11, pp. 88–95.

Pawlak Z. Vagueness – a Rough Set View, Structures in Logic and Computer Science, 1997, pp. 106–117.

Abd El-Monsef M. E., El-Cayar M. A., Aqeel R. M. On relationships between revised rough fuzzy approximation operators and fuzzy topological spaces, International Journal of Granular Computing, Rough Sets and Intelligent Systems, 2014, Volume 3, No. 4, pp. 257–269.

#### GOST Style Citations

1. Граб М. В. Модели, методы и алгоритмы распространения лесных пожаров: дисс. канд. техн. наук: 01.05.02 / Граб Марина Витальевна. – Харьков : ХНУРЭ, 2004. – 230 с.

2. Martinez J. Human-caused wildfire risk rating for prevention planning in Spain / J. Martinez, C. Vega-Garcia, E. Chuvieco // Journal of Environmental Management. –2009. – № 90. –P.1241–1252.

3. Implementation of quantitative bushfire risk analysis in a GIS environment / [D. Atkinson, M. Chladil, V. Janssen et al]. // International Journal of Wildland Fire.–2010. – № 19 –P. 649–658.

4. Chuvieco E. Development of a framework for fire risk assessment usong remote sensing and geographic information system technologies / E. Chuvieco et al. // Ecological Modelling.–2010.–№ 221.–P. 46–58.

5. Preisler H. K. Probability based models for estimating wildfire risk / H. K. Preisler, D. R. Brillinger, R. E. Burgan et al. // International Journal of Wildland Fire. – 2004. № 13. P. 133–142.

6. Spatiotemporal analysis of wildfire ignitions in the St Johns River Water Management District, Florida / [M.G. Genton, D. T. Butry, M. L. Prestemon et al.] // International Journal of Wildland Fire. – 2006. – № 15. – P. 87–97.

7. Baranovskiy N. A web-oriented geoinformation system for forest fire danger prediction in typical forests of the Ukraine / N. Baranovskiy, M. Zharikova // Thematic cartography for the society. – Springer, 2014. – P. 13–22. – (Lecture notes in geoinformation and cartography).

8. Zharikova M. Threat assessment method for intelligent disaster decision support system / M. Zharikova, V. Sherstjuk // Advances in Int. Systems and Computing. – Springer, 2016. – Vol. 512.–P. 81–99.

9. Жарикова М. В. Разработка модели чрезвычайной ситуации природного характера в системе поддержки принятия решений / М. В. Жарикова, В. Г. Шерстюк // Восточно-европейский журнал передовых технологий. – 2015. – 1/4(73). – P. 62–74.

10. An improved cellular automata for wildfire spread / T. Ghisy, B. Arca, G. Pellizaro et al. // Procedia Computer Science: ICCS 2015 International Conference On Computational Science, Reykjavik, Iceland, 1–3 June 2015: proceedings. – Elsevier: Volume 51, 2015. – P. 2287–2296.

11.Modeling the spread of grass fires / [D. H. Anderson, E. Catchpole, De Meste N. J. et al.] // The J. Of the Australian Mathematical Society Series B Applied Mathematics. – 1982. – № 23(4). – P. 451–466.

12. Fire area simulator-model development and evaluation: technical report: RMRS-RP-4 /M. A. Finney / USDA, UT. –Ogden, 2004.–50 p.

13. A level-set algorithm for simulating wildfire spread / [T. Ghisu, B. Arca, G. Pellizzaro et al.]// CMES Computer Modeling in Engineering & Sciences. – 2014. - № 102(1). – P. 83–102.

14. A new algorithm for simulating wildfire through cellular automata / [G. A. Trufino, D. D.’ Ambrosio, R. Rongo et al.] // ACM Transactions on Modeling and Computer Simulation. – 2011.–№ 22(1).–P. 1–26.

15. Zharikova M. The plausible wildfire model in geoinformation decision support system for wildfire response / M. Zharikova, V. Sherstjik, N. Baranovskiy // Water resources, Forest, marine and ocean ecosystems: 15th international conference SGEM, 18 – 24 June 2015: proceedings. – Albena, Bulgaria: DOI: 10.5593/sgem2015B32, 2015.–P. 575–583.

16. Zharikova M. Development of the model of natural emergencies in decision support system / M. Zharikova, V. Sherstjik // EasternEuropean Journal of Enterprise Technologies. – 2015. –Vol 1,№ 4(73). – P. 62–69.

17. Slowinski R. Rough set and rule-based multicriteria decision aiding / R. Slowinski, G. Salvatore, M. Benedetto // Pesquisa operacional. –2012. – Vol. 32(2). – P. 213–269.

18. Pawlak Z. Rough Sets / Z. Pawlak, W. Jerzy, R. Slowinski et al. / / Comm. of ACM. – 1995. – Vol.38, № 11. – P. 88–95.

19. Pawlak Z. Vagueness – a Rough Set View / Z. Pawlak // Structuresin Logic and Computer Science. – 1997. – P. 106–117.

20. Abd El-Monsef M. E. On relationships between revised rough fuzzy approximation operators and fuzzy topological spaces /M. E. Abd El-Monsef, M. A. El-Cayar, R.M. Aqeel // International Journal of Granular Computing, Rough Sets and Intelligent Systems. – 2014. – Volume 3, № 4. – P. 257–269.

Copyright (c) 2017 М. В. Жарікова

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

**Address of the journal editorial office:**

Editorial office of the journal «Radio Electronics, Computer Science, Control»,

Zaporizhzhya National Technical University,

Zhukovskiy street, 64, Zaporizhzhya, 69063, Ukraine.

Telephone: +38-061-769-82-96 – the Editing and Publishing Department.

E-mail: rvv@zntu.edu.ua

**The reference to the journal is obligatory in the cases of complete or partial use of its materials.**