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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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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