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Herald of the Russian Academy of Sciences

, Volume 89, Issue 4, pp 405–417 | Cite as

A Model-Oriented System for Operational Forecasting of River Floods

  • V. A. ZelentsovEmail author
  • A. M. AlabyanEmail author
  • I. N. KrylenkoEmail author
  • I. Yu. PimanovEmail author
  • M. R. PonomarenkoEmail author
  • S. A. PotryasaevEmail author
  • A. E. SemenovEmail author
  • V. A. SobolevskiiEmail author
  • B. V. SokolovEmail author
  • R. M. YusupovEmail author
Environmental Problems
  • 33 Downloads

Abstract

This article gives the results of developing and testing a system for operational river flood forecasting, which is based on a system of hydrological and hydrodynamic models, as well as ground-observation and satellite data. This system is implemented on the basis of service-oriented architecture. A specific feature of the system is fully automated implementation of the entire modeling cycle—from loading input data to interpreting and visualizing the results and alerting the interested parties. The theoretical basis for coherent functioning of all system components is the qualimetry of models and polymodel complexes developed by the authors. The practical implementation is based on open codes and freeware. The results of testing demonstrate the potential for a wide introduction of such systems in the activities of territorial authorities and emergency services.

Keywords:

floods complex simulation operational forecasting service-oriented architecture artificial neural networks hydrodynamic models geoinformation systems. 

Notes

ACKNOWLEDGMENTS

The authors are grateful to colleagues from the Northern Department of the Hydrometeoservice of the Russian Federation and its Vologda branch, primarily, E.N. Skripnik and I.I. Rimmer for their help in experimental studies and interested discussions of the results of the study. The help of the leaders of the development of software complexes ECOMAG and STREAM-2D, Yu.G. Motovilov and V.V. Belikov (Institute of Water Problems, Russian Academy of Sciences) was also invaluable.

FUNDING

The studies for the development of models based on an artificial neural network, the use of the multimodel approach, and experimental studies for testing the system along the Northern Dvina River were supported by the Russian Science Foundation, project no. 17-11-01254. The study for the choice of technologies for developing web services were carried out under budget project no. 0073-2019-0004. Processing ERS data was supported by the project Speeding up Copernicus-Based Innovation in the Baltic Sea Region (BalticSatApps) under the program INTERREG Baltic Sea Region.

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Copyright information

© Pleiades Publishing, Ltd. 2019

Authors and Affiliations

  1. 1.St. Petersburg Institute for Informatics and Automation, Russian Academy of SciencesSt. PetersburgRussia
  2. 2.Lomonosov Moscow State UniversityMoscowRussia
  3. 3.Water Problems Institute, Russian Academy of SciencesMoscowRussia

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