Сибирский федеральный университет

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: International Conference on Knowledge - Based and Intelligent Information and Engineering Systems, KES 2017

Год издания: 2017

Идентификатор DOI: 10.1016/j.procs.2017.08.179

Ключевые слова: cluster analysis, comprehensive multidimensional analysis, data mining, prevention of emergencies, principal component analysis, territorial management, terrritory safety evaluation

Аннотация: This paper presents an approach to evaluating the natural and technogenic safety of the one of the largest regions in Siberia through the comprehensive analysis of territorial indicators in order to explore geographical variations and patterns in occurrence of emergencies by applying the data mining techniques - principal component analysis and cluster analysis - to data of the Territory Safety Passports. For data modeling, two principal components are selected and interpreted taking account of the contribution of the data attributes to the principal components. Data distribution on the principal components is analyzed at different levels of the territory detail: municipal areas and settlements. Two- and three- cluster structures are constructed in multidimensional data space; the main clusters features are investigated. The results of this analysis have allowed to identify the high-risk territories and rank them according to danger degree of occurrence of the natural and technogenic emergencies. This evaluation gives the basis for decision making and makes it possible for authorities to allocate the forces and means for territory protection more efficiently and develop a system of measures to prevent and mitigate the consequences of emergencies in the large region. The suggested in this work approach in terms of its stages, techniques and reasoning procedures can be considered as a model of comprehensive multidimensional analysis of the control objects in various areas. © 2017 The Author(s).

Журнал: Procedia Computer Science

Выпуск журнала: Vol. 112

Номера страниц: 99-108

ISSN журнала: 18770509

Издатель: Elsevier B.V.

• Penkova T.G. (Institute of Computational Modelling of the Siberian Branch, Russian Academy of Sciences, 50/44 Akademgorodok, Krasnoyarsk, Russian Federation, Siberian Federal University, 79 Svobodny pr., Krasnoyarsk, Russian Federation)

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