Creation of Intelligent Information Flood Forecasting Systems Based on Service Oriented Architecture

  • Viacheslav A. ZelentsovEmail author
  • Semyon A. Potryasaev
  • Ilja J. Pimanov
  • Sergey A. Nemykin
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 466)


In this paper a new approach to the creation of short-term forecasting systems of river flooding is being further developed. It provides highly accurate forecasting results due to operative obtaining and integrated processing of the remote sensing and ground-based water flow data in real time. Forecasting of flood areas and depths is performed on a time interval of 12–48 h to be able to perform the necessary steps to alert and evacuate the population. Forecast results are available as web services. The proposed system extends the traditional separate methods based on satellite monitoring or modeling of a river’s physical processes, by using an interdisciplinary approach, integration of different models and technologies, and through intelligent choice of the most suitable models for a flood forecasting.


Modelling Flood forecasting system Service oriented architecture Interdisciplinary approach Intelligent interface 



The research described in this paper is partially supported by the Russian Foundation for Basic Research (grants 15-07-08391, 15-08-08459, 16-07-00779, 16-08-00510, 16-08-01277), grant 074-U01 (ITMO University), project 6.1.1 (Peter the Great St. Petersburg Polytechnic University) supported by Government of Russian Federation, Program STC of Union State “Monitoring-SG” (project 1.4.1-1), The Russian Science Foundation (project 16-19-00199), Department of nanotechnologies and information technologies of the RAS (project 2.11), state research 0073–2014–0009, 0073–2015–0007.


  1. 1.
    Transboundary Flood Risk Management in the UNECE Region. United Nations New York and Geneva (2009)Google Scholar
  2. 2.
    Porfiriev, B.N.: Economic consequences of the 2013 catastrophic flood in the Far East. Herald Russ. Acad. Sci. 85, 40 (2015)CrossRefGoogle Scholar
  3. 3.
    Alekseevskii, N.I., Frolova, N.L., Khristoforov, A.V.: Monitoring Hydrological Processes and Improving Water Management Safety. Izd. Mos. Gos. Univ., Moscow (2011) [in Russian]Google Scholar
  4. 4.
    Vasil’ev, O.F.: Designing systems of operational freshet and high water prediction. Herald Russ. Acad. Sci. 82, 129 (2012)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Sokolov, B.V., Zelentsov, V.A., Mochalov, V.F., Potryasaev, S.A., Brovkina, O.V.: Complex objects remote sensing monitoring and modelling: methodology, technology and practice. In: Proceedings of the 8th EUROSIM Congress on Modelling and Simulation, 10–13 Sept 2013, Cardiff, Wales, United Kingdom, pp. 443–447 (2013)Google Scholar
  6. 6.
    Merkuryev, Y., Merkuryeva, G., Sokolov, B., Zelentsov, V. (eds.): Information Technologies and Tools for Space-Ground Monitoring of Natural and Technological Objects. Riga Technical University, Riga (2014)Google Scholar
  7. 7.
    Sokolov, B.V., Pashchenko, A.Ev., Potryasaev, S.A., Ziuban, A.V., Zelentsov, V.A.: Operational flood forecasting as a web-service. In.: Proceedings of the 29th European Conference on Modelling and Simulation (ECMS 2015), Albena (Varna), Bulgaria. pp. 364–370 (2015)Google Scholar
  8. 8.
    LISPFLOOD-FP, University of Bristol, School of Geographical Sciences, Hydrology Group, (Accessed: 27 April 2013)
  9. 9.
    Belikov, V.V., Krylenko, I.N., Alabyan, A.M., Sazonov, A.A., Glotko, A.V.: Two-dimensional hydrodynamic flood modelling for populated valley areas of Russian rivers. In: Proceedings International Association of Hydrological Sciences, pp. 69–74 (2015)Google Scholar
  10. 10.
    Skotner, C. et al.: MIKE FLOOD WATCHmanaging real-time forecasting, (Accessed: 09 Sept 2015)
  11. 11.
    Delft3D-FLOW Version 3.06 User Manual, (Accessed: 22 Sept 2015)
  12. 12.
    HEC-RAS river analysis system User’s Manual, (Accessed: 22 Sept 2015)
  13. 13.
    Bukatova, I.L.: Evolutionary Modeling and Its Applications. Nauka, Moscow (1979) [in Russian]Google Scholar
  14. 14.
    Sokolov, B.V., Yusupov, R.M.: Conceptual foundations of quality estimation and analysis for models and multiplemodel systems. J. Comput. Syst. Sci. Int. 43(6), 831 (2004)zbMATHGoogle Scholar
  15. 15.
    Merkuryev, Y., Okhtilev, M., Sokolov, B., Trusina, I., Zelentsov, V.: Intelligent technology for space and ground based monitoring of natural objects in cross-border EU-Russia territory. In: Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS 2012), Munich, Germany, pp. 2759–2762 (2012)Google Scholar
  16. 16.
    Baryshnikov, N.B.: Hydraulic Resistances of Riverbeds, Izd. Ross. Gos. Gidrometeorol. Univ., St. Petersburg (2003) [in Russian]Google Scholar
  17. 17.
    Sokolov, B.V., Zelentsov, V.A., Brovkina, O. et al.: Complex objects remote sensing forest monitoring and modeling. In: Silhavy, R., Senkerik, R., Oplatkova, Z.K., Silhavy, P., Prokopova, Z. (eds.) Modern Trends and Techniques in Computer Science: Advances in Intelligent Systems and Computing, vol. 285, pp. 445–453. Springer (2014)Google Scholar
  18. 18.
    Sokolov, B.V., Zelentsov, V.A., Yusupov, R.M., Merkuryev, Yu.A.: Multiple models of information fusion processes: quality definition and estimation. J. Comput. Sci. 5, 380 (2014)CrossRefGoogle Scholar
  19. 19.
    Vasiliev, Y.: SOA and WS-BPEL: Composing Service-Oriented Solution with PHP and ActiveBPEL. Packt Publishing (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (, which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  • Viacheslav A. Zelentsov
    • 1
    Email author
  • Semyon A. Potryasaev
    • 1
  • Ilja J. Pimanov
    • 1
  • Sergey A. Nemykin
    • 2
  1. 1.Russian Academy of Sciences, Saint Petersburg Institute of Informatics and Automation (SPIIRAS)St. PetersburgRussia
  2. 2.Design Bureau “Arsenal” Named M.V.FrunzeSaint-PetersburgRussia

Personalised recommendations