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Data-Driven Modelling: Concepts, Approaches and Experiences

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Practical Hydroinformatics

Part of the book series: Water Science and Technology Library ((WSTL,volume 68))

Abstract

Data-driven modelling is the area of hydroinformatics undergoing fast development. This chapter reviews the main concepts and approaches of data-driven modelling, which is based on computational intelligence and machine-learning methods. A brief overview of the main methods – neural networks, fuzzy rule-based systems and genetic algorithms, and their combination via committee approaches – is provided along with hydrological examples and references to the rest of the book.

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Author information

Authors and Affiliations

  1. UNESCO-IHE Institute for Water Education, P.O. Box 3015, 2601 DA Delft, The Netherlands

    D. Solomatine

  2. School of Geography, University of Leeds, Woodhouse Lane, Leeds, LS2 9JT, UK

    L.M. See

  3. School of Geography, University of Nottingham, Nottingham, NG7 2RD, UK

    R.J. Abrahart

Authors
  1. D. Solomatine
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  2. L.M. See
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  3. R.J. Abrahart
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Editor information

Editors and Affiliations

  1. Dept. Geography, University Nottingham, University Park, Nottingham, United Kingdom NT7 2QW

    Robert J. Abrahart

  2. School of Geography Fac. Earth and Environment, University of Leeds, Woodhouse Lane, Leeds, United Kingdom LS2 9JT

    Linda M. See

  3. UNESCO - IHE, Institute for Water Education, 2601 DA Delft, The Netherlands

    Dimitri P. Solomatine

  4. Water Resources Section, Delft University of Technology, The Netherlands

    Dimitri P. Solomatine

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Solomatine, D., See, L., Abrahart, R. (2009). Data-Driven Modelling: Concepts, Approaches and Experiences. In: Abrahart, R.J., See, L.M., Solomatine, D.P. (eds) Practical Hydroinformatics. Water Science and Technology Library, vol 68. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79881-1_2

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