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The Use of Artificial Neural Networks to Analyze and Predict Alongshore Sediment Transport : Volume 17, Issue 5 (02/09/2010)

By Van Maanen, B.

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Book Id: WPLBN0003976127
Format Type: PDF Article :
File Size: Pages 10
Reproduction Date: 2015

Title: The Use of Artificial Neural Networks to Analyze and Predict Alongshore Sediment Transport : Volume 17, Issue 5 (02/09/2010)  
Author: Van Maanen, B.
Volume: Vol. 17, Issue 5
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2010
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Bryan, K. R., Coco, G., Ruessink, B. G., & Maanen, B. V. (2010). The Use of Artificial Neural Networks to Analyze and Predict Alongshore Sediment Transport : Volume 17, Issue 5 (02/09/2010). Retrieved from http://hawaiilibrary.net/


Description
Description: National Institute of Water and Atmospheric Research (NIWA), P.O. Box 11-115, Hamilton, New Zealand. An artificial neural network (ANN) was developed to predict the depth-integrated alongshore suspended sediment transport rate using 4 input variables (water depth, wave height and period, and alongshore velocity). The ANN was trained and validated using a dataset obtained on the intertidal beach of Egmond aan Zee, the Netherlands. Root-mean-square deviation between observations and predictions was calculated to show that, for this specific dataset, the ANN (Εrms=0.43) outperforms the commonly used Bailard (1981) formula (Εrms=1.63), even when this formula is calibrated (Εrms=0.66). Because of correlations between input variables, the predictive quality of the ANN can be improved further by considering only 3 out of the 4 available input variables (Εrms=0.39). Finally, we use the partial derivatives method to open and lighten the generated ANNs with the purpose of showing that, although specific to the dataset in question, they are not black-box type models and can be used to analyze the physical processes associated with alongshore sediment transport. In this case, the alongshore component of the velocity, by itself or in combination with other input variables, has the largest explanatory power. Moreover, the behaviour of the ANN indicates that predictions can be unphysical and therefore unreliable when the input lies outside the parameter space over which the ANN has been developed. Our approach of combining the strong predictive power of ANNs with lightening the black box and testing its sensitivity, demonstrates that the use of an ANN approach can result in the development of generalized models of suspended sediment transport.

Summary
The use of artificial neural networks to analyze and predict alongshore sediment transport

Excerpt
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