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Modeling flood zones on the basis of time series forecasting and GIS-technologies by the example of the Republic of Bashkortostan
E.V. Palchevsky 1, V.V. Antonov 2, L.E. Rodionova 2, L.A. Kromina 2, A.R. Fakhrullina 2

1 Financial University under the Government of the Russian Federation,
109456, Russia, Moscow, st. 4th Veshnyakovsky passage 4;
2 Ufa University of Science and Technology,
450008, Russia, Ufa, st. Karl Marx 12

 PDF, 2398 kB

DOI: 10.18287/2412-6179-CO-1418

Pages: 913-923.

Full text of article: Russian language.

Abstract:
A specialized GIS Web is proposed, implemented by integrating an artificial neural network and geotechnologies and providing early forecasting and modeling of flood zones up to five days in advance.
     The methods and algorithms implemented within this GIS Web allow daily forecasting of time series based on retrospective data on water levels and total water inflow, air and water temperature, snow cover thickness and precipitation, wind speed and atmospheric pressure. At the same time, the possibility of early modeling and visualization of river floods is realized only on the basis of the obtained predictive values of the water level. This will enable specialized organizations and services, as well as management bodies to make decisions related to flood control measures in advance and as soon as possible.

Keywords:
geographic information system, flood zone modelling, time series forecasting, artificial neural networks.

Citation:
Palchevsky EV, Antonov VV, Rodionova LE, Kromina LA, Fakhrullina AR. Modeling flood zones on the basis of time series forecasting and GIS-technologies . Computer Optics 2024; 48(6): 913-923. DOI: 10.18287/2412-6179-CO-1418.

Acknowledgements:
The work was financially supported by the Ministry of Science and Higher Education of the Russian Federation within the main part of the state project for Higher Education institutions, # FEUE 2023-0007.
The authors wish to thank the Federal State Unitary Enterprise "Center for Register and Cadastre" for providing archival temporal data.

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