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DC Field | Value | Language |
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dc.contributor.author | Гаврись, Андрій Петрович | - |
dc.contributor.author | Яковчук, Роман Святославович | - |
dc.contributor.author | Пекарська, Олександра Олексіівна | - |
dc.date.accessioned | 2023-07-10T13:36:24Z | - |
dc.date.available | 2023-07-10T13:36:24Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Havrys, A., Yakovchuk, R., Pekarska, O., Tur, N. (2023). Visualization of fire in space and time on the basis of the method of spatial location of fire-dangerous areas. Ecological Engineering & Environmental Technology, 24(2). 1. Abdulsahib, G.M., Khalaf, O.I. 2018. An improved algorithm to fire detection in forest by using wireless sensor networks. International Journal of Civil Engineering & Technology (IJCIET)-Scopus Indexed, 9(11), 369–377. 2. ArcGIS Resources. 2012. ArcGIS Help 10.1. [online] Available at: http://resources.arcgis.com/en/help/ main/10.1/index.html#/ [Accessed 19 May 2021]. 3. Atwood, E.C., Englhart, S., Lorenz, E., Halle, W., Wiedemann, W., Siegert, F. 2016. Detection and Characterization of Low Temperature Peat Fires during the 2015 Fire Catastrophe in Indonesia Using a New High-Sensitivity Fire Monitoring Satellite Sensor (FireBird). PLoS ONE, 11, e0159410. 4. EARTHDATA. 2020. Active Fire Data | Earthdata. [online] Available at: https://earthdata.nasa. gov/earth-observation-data/near-real-time/firms/ active-fire-data. 5. Havrys, A.P., Moreniuk, R.Y., Harasymiuk, I.M. 2019. Method of spatial location of fire-dangerous sites on the basis of Remote Sensing and Spatial Data. Scientific bulletin of UNFU, 29(8), 36–42. [in Ukrainian] https://doi.org/10.36930/40290804 6. Iizuka, K., Watanabe, K., Kato, T., Putri, N. A., Silsigia, S., Kameoka, T., & Kozan, O. 2018. Visualizing the spatiotemporal trends of thermal characteristics in a peatland plantation forest in Indonesia: Pilot test using unmanned aerial systems (UASs). Remote Sensing, 10(9), 1345. 7. Li, J., Li, X., Chen, C., Zheng, H., Liu, N. 2018. Three-dimensional dynamic simulation system for forest surface fire spreading prediction. International Journal of Pattern Recognition and Artificial Intelligence, 32(8), 1850026. 8. Li, P., Zhao, W. 2020. Image fire detection algorithms based on convolutional neural networks. Case Studies in Thermal Engineering, 19, 100625. 9. Liu, D., Xu, Z., Fan, C. 2019. Generalized analysis of regional fire risk using data visualization of incidents. Fire and materials, 43(4), 413–421. 10. Luo, Y., Zhao, L., Liu, P., Huang, D. 2018. Fire smoke detection algorithm based on motion characteristic and convolutional neural networks. Multimedia Tools and Applications, 77(12), 15075–15092. 11. Muhammad, K., Khan, S., Elhoseny, M., Ahmed, S.H., Baik, S.W. 2019. Efficient fire detection for uncertain surveillance environment. IEEE Transactions on Industrial Informatics, 15(5), 3113–3122. 12. Nikolaevich, K.V., Starodub, Y., Havrys, A. 2021. Computer Modeling in the Application to Geothermal Engineering. Advances in Civil Engineering, 2021. | en_US |
dc.identifier.uri | https://sci.ldubgd.edu.ua/jspui/handle/123456789/11849 | - |
dc.description.abstract | The subject of the study is the forecasting of fires using the spatial location of fire-hazardous areas. To do this, several approaches were used to visualize data in space and time. A temporary map has been created showing the points of fires using a color scheme linked to the date. A series of small multiple visualizations has been developed. A time series has been created in which the regularity of the brightness of points is distributed depending on the date of origin and animated maps that allow you to view data in space and time. In this case, the geographic information system was used as the main tool when working with maps, as it is one of the best ways to process georeferenced data displayed on the map. A space-time cube is displayed, which displays data in 3D format, or rather, fire points, symbolized by the average temperature of the fire (displayed in different colors) in accordance with the day of the month. | en_US |
dc.language.iso | ua | en_US |
dc.publisher | Challenges and threats to critical infrastructure. Collective monograph - NGO Institute for Cyberspace Research (Detroit, Michigan, USA), 2023. - pp. 215-219. | en_US |
dc.subject | цивільна безпека | en_US |
dc.subject | моделювання | en_US |
dc.title | Візуалізація пожеж у просторі та часі на основі методу просторового розміщення пожежонебезпечних ділянок | en_US |
Appears in Collections: | 2023 |
Files in This Item:
File | Description | Size | Format | |
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Тези в НДІ ЦЗ 2023.pdf | 1.04 MB | Adobe PDF | View/Open |
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