Version 1
: Received: 6 March 2024 / Approved: 7 March 2024 / Online: 7 March 2024 (07:50:58 CET)
How to cite:
Savchenko, E.; Maklakov, S. Methodology for identifying natural phenomena that form large floods using satellite data. Preprints2024, 2024030411. https://doi.org/10.20944/preprints202403.0411.v1
Savchenko, E.; Maklakov, S. Methodology for identifying natural phenomena that form large floods using satellite data. Preprints 2024, 2024030411. https://doi.org/10.20944/preprints202403.0411.v1
Savchenko, E.; Maklakov, S. Methodology for identifying natural phenomena that form large floods using satellite data. Preprints2024, 2024030411. https://doi.org/10.20944/preprints202403.0411.v1
APA Style
Savchenko, E., & Maklakov, S. (2024). Methodology for identifying natural phenomena that form large floods using satellite data. Preprints. https://doi.org/10.20944/preprints202403.0411.v1
Chicago/Turabian Style
Savchenko, E. and Sergey Maklakov. 2024 "Methodology for identifying natural phenomena that form large floods using satellite data" Preprints. https://doi.org/10.20944/preprints202403.0411.v1
Abstract
In this paper, we studied the problem of early detection of natural phenomena associated with the formation of severe floods and the possibility of constant monitoring of their occurrence. The test object for the study was severe floods on the Crimean Peninsula, which occurred due to heavy rainfall in the summer of 2021. Based on the selected satellite and other data related to this period, an analysis carried out and key atmospheric objects identified, according to the proposed algorithm for their identification and grouping. Based on the results of the work, a positive conclusion made on the issue of their use to identify the likelihood of natural disasters that could lead to floods. In addition, the authors proposed to develop a portal for collecting and processing a large amount of satellite data related to flood prevention and monitoring the development of such processes, and stressed the need to create it.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
