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Tatiana, Penkova
2019-07-01T07:26:57Z
2019-07-01T07:26:57Z
2018-09
Tatiana, Penkova. Multidimensional Data Analysis for Evaluating the Natural and Anthropogenic Safety (in the Case of Krasnoyarsk Territory) [Текст] / Penkova Tatiana // Lecture Notes in Artificial Intelligence: International Joint Conference on Rough Sets (IJCRS 2018). — 2018. — Т. 11103. — С. 101-109
https://link.springer.com/book/10.1007/978-3-319-99368-3
https://elib.sfu-kras.ru/handle/2311/110867
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 the multidimensional data analysis technique is applied to data of the Territory Safety Passports. For data modeling, 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. The results of this analysis have allowed to identify the high-risk areas and rank the territories according to danger degree of occurrence of the natural and technogenic emergencies. It 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.
Multidimensional data analysis
Principal component analysis
Evaluating the natural and anthropogenic safety
Prevention of emergencies
Territorial management
Multidimensional Data Analysis for Evaluating the Natural and Anthropogenic Safety (in the Case of Krasnoyarsk Territory)
Journal Article
Journal Article Preprint
101-109
20.53.19
2019-07-01T07:26:57Z
10.1007/978-3-319-99368-3_8
Институт космических и информационных технологий
Кафедра информатики
Lecture Notes in Artificial Intelligence
Q2
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