River Flood Forecasting System: An Interdisciplinary Approach

  • Viacheslav Zelentsov
  • Ilya Pimanov
  • Semen Potryasaev
  • Boris Sokolov
  • Sergey Cherkas
  • Andrey Alabyan
  • Vitaly Belikov
  • Inna Krylenko
Chapter
Part of the Springer Remote Sensing/Photogrammetry book series (SPRINGERREMO)

Abstract

The chapter presents a holistic system that implements an advanced river flood modeling and forecasting approach. This approach extends traditional methods based on separate satellite monitoring or river physical processes modeling, by integration of different technologies such as satellite and in situ data processing, input data clustering and filtering, digital mapping of river valleys relief, data crowdsourcing, hydrodynamic modeling, inundation visualization, and also duly warning of stakeholders.

The software of the suggested system was implemented on the base of open source code and service-oriented architecture (SOA). This allows the use of different program modules for data processing and modeling, integrated into a unified software suite. Forecast results are available as web services. Additionally, a special GIS platform has been developed to visualize the results of forecasting. It does not require the users to have any special skills or knowledge, and all the complexity relating to data processing and modeling is hidden from the users.

The results of case studies have shown that the suggested interdisciplinary approach provides highly accurate forecasting due to operational ingestion and integrated processing of the remote sensing and ground-based water flow data in real time. In these case studies, forecasting of flood areas and depths was performed on a time interval of 12–48 h, allowing performing the necessary steps to alert and evacuate the population.

In general, the developed software can be considered as a toolkit to create holistic monitoring and flood forecasting systems with an integrated use of diverse satellite and in situ data.

Keywords

River floods Interdisciplinary approach Multimodel simulation Operational forecasting Remote sensing data Intelligent interface Service oriented architecture 

Notes

Acknowledgments

The research described in this chapter 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, 16-29-09482-оfi-i, 16-07-00925, 17-08-00797, 17-06-00108, 17-01-00139, 17-20-01214), by the Russian Research Foundation (grants 16-19-00199, 17-11-01254), by ITMO University’s grant 074-U01, 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), state order of the Ministry of Education and Science of the Russian Federation №2.3135.2017/K, and state research 0073–2014–0009 and 0073–2015–0007.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Viacheslav Zelentsov
    • 1
  • Ilya Pimanov
    • 1
  • Semen Potryasaev
    • 1
  • Boris Sokolov
    • 1
  • Sergey Cherkas
    • 1
  • Andrey Alabyan
    • 2
  • Vitaly Belikov
    • 2
  • Inna Krylenko
    • 2
  1. 1.Saint Petersburg Institute of Informatics and Automation of the Russian Academy of SciencesSt. PetersburgRussia
  2. 2.Lomonosov Moscow State UniversityMoscowRussia

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