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Neural Network Estimation of Suspended Sediment: Potential Pitfalls and Future Directions

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Practical Hydroinformatics

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

Abstract

This chapter examines two neural network approaches for modelling suspended sediment concentration at different temporal scales: daily-record and flood-event. Four daily-record models are developed for the USGS gauging station at Quebrada Blanca near Jagual in Puerto Rico previously used by kisi (2005) for estimating suspended sediment concentration: comparisons with that earlier investigation are presented. The flood-event approach is trialled on records for the EA gauging station at Low Moor on the River Tees in northern England. The power of neural networks to perform different types of modelling operation and to develop reasonable results in the two test cases is highlighted. Event-based modelling of mean suspended sediment concentration is a novel concept that warrants further trialling and testing on different international catchments or data sets.

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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 & A.J. Heppenstall

  3. Integrated Environmental Systems Institute, Cranfield University, College Road, Cranfield, Bedfordshire, MK43 OAL, UK

    S.M. White

Authors
  1. R.J. Abrahart
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  2. L.M. See
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  3. A.J. Heppenstall
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  4. S.M. White
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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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Abrahart, R., See, L., Heppenstall, A., White, S. (2009). Neural Network Estimation of Suspended Sediment: Potential Pitfalls and Future Directions. 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_11

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