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Water 2016, 8(2), 53; doi:10.3390/w8020053

Estimating Sediment Discharge Using Sediment Rating Curves and Artificial Neural Networks in the Shiwen River, Taiwan

Department of Civil Engineering, National Pingtung University of Science and Technology, Pingtung 912, Taiwan
These authors contributed equally to this work.
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Author to whom correspondence should be addressed.
Academic Editors: Jochen Aberle and Nils Ruther
Received: 30 December 2015 / Revised: 26 January 2016 / Accepted: 2 February 2016 / Published: 5 February 2016
(This article belongs to the Special Issue Watershed Sediment Process)
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Abstract

Sediment in river is usually transported during extreme events related to intense rainfall and high river flows. The conventional means of collecting data in such events are risky and costly compared to water discharge measurements. Hence, the lack of sediment data has prompted the use of sediment rating curves (SRC). The aim of this study is to explore the abilities of artificial neural networks (ANNs) in advancing the precision of stream flow-suspended discharge relationships during storm events in the Shiwen River, located in southern Taiwan. The ANNs used were multilayer perceptrons (MLP), the coactive neurofuzzy inference system model (CANFISM), time lagged recurrent networks (TLRN), fully recurrent neural networks (FRNN) and the radial basis function (RBF). A comparison is made between SRC and the ANNs. Hourly based water and sediment discharge during 8 storms were manually collected and used as inputs for the SRC and the ANNs. Results have shown that the ANN models were superior in reproducing hourly sediment discharge compared to SRC. The findings further suggest that MLP can provide the most accurate estimates of sediment discharge, (R2 of 0.903) compared to CANFISM, TLRN, FRNN and RBF. SRC had the lowest R2 (0.765), and resulted in underestimations of peak sediment discharge (−47%). View Full-Text
Keywords: sediment discharge; sediment rating curve; neural network; Shiwen River sediment discharge; sediment rating curve; neural network; Shiwen River
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Tfwala, S.S.; Wang, Y.-M. Estimating Sediment Discharge Using Sediment Rating Curves and Artificial Neural Networks in the Shiwen River, Taiwan. Water 2016, 8, 53.

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