THE MODEL OF TERITORIAL SYSTEM IN NATURAL DISASTER CONDITIONS
Keywords:Territorial system, indiscernibility relation, topological space, equivalence class, geotaxon, natural emergency
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.
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.
How to Cite
Copyright (c) 2017 М. В. Жарікова
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Creative Commons Licensing Notifications in the Copyright Notices
The journal allows the authors to hold the copyright without restrictions and to retain publishing rights without restrictions.
The journal allows readers to read, download, copy, distribute, print, search, or link to the full texts of its articles.
The journal allows to reuse and remixing of its content, in accordance with a Creative Commons license СС BY -SA.
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License CC BY-SA that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.