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Article

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

by and *,†
Department of Civil Engineering, National Pingtung University of Science and Technology, Pingtung 912, Taiwan
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Jochen Aberle and Nils Ruther
Water 2016, 8(2), 53; https://doi.org/10.3390/w8020053
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)
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
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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. https://doi.org/10.3390/w8020053

AMA Style

Tfwala SS, Wang Y-M. Estimating Sediment Discharge Using Sediment Rating Curves and Artificial Neural Networks in the Shiwen River, Taiwan. Water. 2016; 8(2):53. https://doi.org/10.3390/w8020053

Chicago/Turabian Style

Tfwala, Samkele S., and Yu-Min Wang. 2016. "Estimating Sediment Discharge Using Sediment Rating Curves and Artificial Neural Networks in the Shiwen River, Taiwan" Water 8, no. 2: 53. https://doi.org/10.3390/w8020053

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