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Predicting Sediment Concentrations Using a Nonlinear Autoregressive Exogenous Neural Network

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Computational Science and Its Applications – ICCSA 2019 (ICCSA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11621))

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Abstract

An application of a Nonlinear Autoregressive Exogenous Neural Network (NARX) to predict total suspended sediment concentrations (SST) for a water body located in central Chile (Francia Creek, Valparaiso) is presented. Input data consisting of precipitation and stream flow time-series were fed to the developed NARX, for prediction of daily SST concentrations for a whole year. Sensitivity analysis was used for achieving the best NARX configuration that provided the best fit of simulated vs. measured data for year 2014. Parameters varied during sensitivity analysis were: number of nodes, number of iterations, feedback and forward delays, years of daily data used as training dataset. The resulting NARX is an open-loop net, consisting of a 12-node hidden layer, 100-iterations, using the Bayesian regularization backpropagation algorithm. SST concentrations predicted by the NARX net agreed successfully with measured SST concentrations (r = 0.73, r2 = 0.53, NSE = 0.18, PBIAS = −13.6%, Index of Agreement = 0.87).

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Authors and Affiliations

  1. Civil Engineering Department, Universidad Diego Portales, 441 Ejercito Avenue, Santiago, Chile

    Vladimir J. Alarcon

Authors
  1. Vladimir J. Alarcon
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Correspondence to Vladimir J. Alarcon .

Editor information

Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  3. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Saint Petersburg State University, Saint Petersburg, Russia

    Vladimir Korkhov

  6. Polytechnic University of Bari, Bari, Italy

    Carmelo Torre

  7. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  8. Monash University, Clayton, VIC, Australia

    David Taniar

  9. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  10. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

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Alarcon, V.J. (2019). Predicting Sediment Concentrations Using a Nonlinear Autoregressive Exogenous Neural Network. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11621. Springer, Cham. https://doi.org/10.1007/978-3-030-24302-9_42

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  • DOI: https://doi.org/10.1007/978-3-030-24302-9_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24301-2

  • Online ISBN: 978-3-030-24302-9

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