TY  - JOUR
EP  - 1897
SN  - 09204741
PB  - Kluwer Academic Publishers
N1  - cited By 65
SP  - 1879
TI  - River suspended sediment prediction using various multilayer perceptron neural network training algorithms-A case study in Malaysia
AV  - none
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84860486004&doi=10.1007%2fs11269-012-9992-5&partnerID=40&md5=ce39080bff7189db1771a75ded47be39
JF  - Water Resources Management
A1  - Mustafa, M.R.
A1  - Rezaur, R.B.
A1  - Saiedi, S.
A1  - Isa, M.H.
VL  - 26
Y1  - 2012///
N2  - Estimation of suspended sediment discharge in rivers has a vital role in dealing with water resources problems and hydraulic structures. In this study, a Multilayer Perceptron (MLP) feed forward neural network with four different training algorithms was used to predict the suspended sediment discharge of a river (Pari River at Silibin) in Peninsular Malaysia. The training algorithms are Gradient Descent (GD), Gradient Descent with Momentum (GDM), Scaled Conjugate Gradient (SCG), and Levenberg Marquardt (LM). Different statistical measures, time of convergence and number of epochs to reach the required accuracy were used to evaluate the performance of training algorithms. The analysis showed that SCG and LM performed better than GD and GDM. While the performance of the superior algorithms (i.e., SCG and LM) is similar, LM required considerably shorter time of convergence. It was concluded that both training algorithms SCG and LM could be recommended for suspended sediment prediction using MLP networks. However, LM was the faster (1/7 of SCG convergence time) of the two algorithms. © Springer Science+Business Media B.V. 2012.
IS  - 7
KW  - Discharge (fluid mechanics); Forecasting; Models; Multilayers; Neural networks; Rivers
KW  -  Levenberg-Marquardt; Multi layer perceptron; Multi-layer perceptron neural networks; River suspended sediments; Scaled conjugate gradients; Statistical measures; Suspended-sediment discharges; Training algorithms
KW  -  Suspended sediments
KW  -  accuracy assessment; algorithm; artificial neural network; discharge; fluvial deposit; geostatistics; gradient analysis; numerical model; performance assessment; prediction; suspended sediment
KW  -  Malaysia; West Malaysia
ID  - scholars3194
ER  -