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Swarm Intelligence Approach for Parametric Learning of a Nonlinear River Flood Routing Model

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Book cover Highlights of Practical Applications of Survivable Agents and Multi-Agent Systems. The PAAMS Collection (PAAMS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1047))

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Abstract

Flood routing models are mathematical methods used to predict the changes over the time in variables such as the magnitude, speed and shape of a flood wave when water moves in a river, a stream or a reservoir. These techniques are widely used in water engineering for flood prediction and many other applications such as dam design, geographic and urban planning, disaster prevention, and so on. Flood routing models typically depend on some parameters that must be estimated from data. Several techniques have been described in the literature for this task. Among them, those based on swarm intelligence are getting increasing attention from the scientific community during the last few years. In this context, the present contribution applies a powerful swarm intelligence technique called bat algorithm to perform parametric learning of a hydrological model for nonlinear river flood routing. The method is applied to data of a real-world example of a river reach with very good results.

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Acknowledgments

The authors acknowledge the financial support from the project PDE-GIR of the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 778035, the project from the Spanish Ministry of Science, Innovation and Universities (Computer Science National Program) under grant #TIN2017-89275-R of the Agencia Estatal de Investigación and European Funds FEDER (AEI/FEDER, UE), and the project #JU12, of SODERCAN and EU Funds FEDER (SODERCAN/FEDER-UE). The last two authors are also grateful to the Department of Information Science of Toho University for all the facilities given to carry out this work.

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Authors and Affiliations

  1. Department of Applied Mathematics and Computational Sciences, University of Cantabria, Avenida de los Castros s/n, 39005, Santander, Spain

    Rebeca Sánchez, Patricia Suárez, Akemi Gálvez & Andrés Iglesias

  2. Department of Information Science, Faculty of Sciences, Narashino Campus, Toho University, 2-2-1 Miyama, Funabashi, 274-8510, Japan

    Akemi Gálvez & Andrés Iglesias

Authors
  1. Rebeca Sánchez
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  2. Patricia Suárez
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  3. Akemi Gálvez
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  4. Andrés Iglesias
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Corresponding author

Correspondence to Andrés Iglesias .

Editor information

Editors and Affiliations

  1. University of Salamanca, Salamanca, Spain

    Fernando De La Prieta

  2. University of Salamanca, Salamanca, Spain

    Alfonso González-Briones

  3. Poznań University of Technology, Poznan, Poland

    Pawel Pawleski

  4. HES-SO Valais Wallis, Sierre, Switzerland

    Davide Calvaresi

  5. Polytechnic University of Valencia, Valencia, Spain

    Elena Del Val

  6. National Laboratory for Energy and Geology, Amadora, Portugal

    Fernando Lopes

  7. Polytechnic University of Valencia, Valencia, Spain

    Vicente Julian

  8. Tecnalia Research & Innovation, Derio, Spain

    Eneko Osaba

  9. University of Murcia, Murcia, Spain

    Ramón Sánchez-Iborra

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Sánchez, R., Suárez, P., Gálvez, A., Iglesias, A. (2019). Swarm Intelligence Approach for Parametric Learning of a Nonlinear River Flood Routing Model. In: De La Prieta, F., et al. Highlights of Practical Applications of Survivable Agents and Multi-Agent Systems. The PAAMS Collection. PAAMS 2019. Communications in Computer and Information Science, vol 1047. Springer, Cham. https://doi.org/10.1007/978-3-030-24299-2_24

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  • DOI: https://doi.org/10.1007/978-3-030-24299-2_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24298-5

  • Online ISBN: 978-3-030-24299-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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