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Title: Principle component analysis in conjuction with data driven methods for sediment load prediction
Authors: Tayfur, Gökmen
Karimi, Yashar
Singh, Vijay P.
Tayfur, Gökmen
Izmir Institute of Technology. Civil Engineering
Keywords: Principle component analysis
Sediment load
Artificial neural network
Genetic algorithms
Issue Date: May-2013
Publisher: Springer Verlag
Source: Tayfur, G., Karimi, Y., and Singh, V.P. (2013). Principle component analysis in conjuction with data driven methods for sediment load prediction. Water Resources Management, 27(7), 2541-2554. doi:10.1007/s11269-013-0302-7
Abstract: This study investigates sediment load prediction and generalization from laboratory scale to field scale using principle component analysis (PCA) in conjunction with data driven methods of artificial neural networks (ANNs) and genetic algorithms (GAs). Five main dimensionless parameters for total load are identified by using PCA. These parameters are used in the input vector of ANN for predicting total sediment loads. In addition, nonlinear equations are constructed, based upon the same identified dimensionless parameters. The optimal values of exponents and constants of the equations are obtained by the GA method. The performance of the so-developed ANN and GA based methods is evaluated using laboratory and field data. Results show that the expert methods (ANN and GA), calibrated with laboratory data, are capable of predicting total sediment load in field, thus showing their transferability. In addition, this study shows that the expert methods are not transferable for suspended load, perhaps due to insufficient laboratory data. Yet, these methods are able to predict suspended load in field, when trained with respective field data.
ISSN: 0920-4741
Appears in Collections:Civil Engineering / İnşaat Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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