Statistical Approach to Model the Deep Draft Ships’ Squat in the St. Lawrence Waterway
Publication: Journal of Waterway, Port, Coastal, and Ocean Engineering
Volume 135, Issue 3
Abstract
In shallow waterways such as the St. Lawrence River, an accurate prediction of the squat is important to ensure a balance between the security and the efficiency of traffic. The Canadian Coast Guard is now studying the squat phenomenon and considering to reassess the actual underkeel clearance standards of the St. Lawrence Waterway. Hence, a field campaign was conducted with 12 deep draft ship sailings, during which the maximal squat was measured with on-the-fly global positioning system. All the variables that may influence the squat (speed, draught, water level, etc.) were also measured. Twenty of the empirical models that are used in practice to predict the squat were tested and the Canadian Coast Guard recommended to either optimize these models or develop new models. Therefore, statistical approaches to model the squat of deep draft ships that navigate on the St. Lawrence Waterway are proposed in this paper. The Eryuzlu model, which is presently used by the Canadian Coast Guard, was optimized by modeling its errors with a stepwise regression. New models were also developed with the regression tree technique. The performance of the statistical models was better than 10 empirical models that are considered the most suitable to predict the maximal squat in the St. Lawrence Waterway. The models built by regression tree gave the best predictions.
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Acknowledgments
The writers wish to thank the Canadian Coast Guard (CCG) and the Natural Sciences and Engineering Research Council of Canada (NSERC)NSERC for funding this research. The writers would also like to thank the CCG for providing the high-quality database and Professor Karem Chokmani of INRS-ETE for his useful comments. The writers would also like to thank the editor, Professor Alexander Härting, and two anonymous reviewers for their valuable comments and suggestions.
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© 2009 ASCE.
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Received: Feb 7, 2008
Accepted: Sep 24, 2008
Published online: Feb 24, 2009
Published in print: May 2009
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