Principle Component Analysis in Conjuction with Data Driven Methods for Sediment Load Prediction

Gokmen Tayfur (), Yashar Karimi () and Vijay Singh ()

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2013, vol. 27, issue 7, 2554 pages

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. Copyright Springer Science+Business Media Dordrecht 2013

Keywords: Principle component analysis; Sediment load; Artificial neural network; Genetic algorithm; Transferability (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/s11269-013-0302-7

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