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Neural Network Hydroinformatics: Maintaining Scientific Rigour

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Practical Hydroinformatics

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

Abstract

This chapter describes the current status of neural network hydrological modelling. Neural network modelling is now a regular feature in most peer-reviewed hydrological and water resource publications. The number of reported operational models is, nevertheless, restricted to a small handful of diverse implementations located in different parts of the world. The social and institutional reasons for this fundamental mismatch are discussed, and a requirement for stronger scientific rigour in modelling and reporting is highlighted. Eight potential guidelines for the development of a stronger scientific foundation are provided.

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Author information

Authors and Affiliations

  1. School of Geography, University of Nottingham, University Park, Nottingham NG7 2RD, UK

    R.J. Abrahart

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

    L.M. See

  3. Department of Computer Science, Loughborough University, Leicestershire, LE11 3TU, UK

    C.W. Dawson

Authors
  1. R.J. Abrahart
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  2. L.M. See
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  3. C.W. Dawson
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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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© 2009 Springer-Verlag Berlin Heidelberg

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Abrahart, R., See, L., Dawson, C. (2009). Neural Network Hydroinformatics: Maintaining Scientific Rigour. 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_3

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