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Statistical approach to model the deep draft ships’ squat in the St. Lawrence Waterway

Statistical approach to model the deep draft ships’ squat in the St. Lawrence Waterway
Statistical approach to model the deep draft ships’ squat in the St. Lawrence Waterway
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.
0733-950X
80-90
Beaulieu, Claudie
13ae2c11-ebfe-48d9-bda9-122cd013c021
Gharbi, Samir
9fb7df82-6c9d-4992-a0b1-65de75042622
Ouarda, Taha B.
2c1ca3fb-37e1-48d7-833b-ad603c0c7ca5
Seidou, Ousmane
68a09e6d-e707-4156-a3ef-2c1ed3819898
Beaulieu, Claudie
13ae2c11-ebfe-48d9-bda9-122cd013c021
Gharbi, Samir
9fb7df82-6c9d-4992-a0b1-65de75042622
Ouarda, Taha B.
2c1ca3fb-37e1-48d7-833b-ad603c0c7ca5
Seidou, Ousmane
68a09e6d-e707-4156-a3ef-2c1ed3819898

Beaulieu, Claudie, Gharbi, Samir, Ouarda, Taha B. and Seidou, Ousmane (2009) Statistical approach to model the deep draft ships’ squat in the St. Lawrence Waterway Journal of Waterway, Port, Coastal, and Ocean Engineering, 135, (3), pp. 80-90. (doi:10.1061/(ASCE)WW.1943-5460.0000003).

Record type: Article

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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More information

e-pub ahead of print date: 24 February 2009
Published date: May 2009
Organisations: Ocean and Earth Science

Identifiers

Local EPrints ID: 352262
URI: http://eprints.soton.ac.uk/id/eprint/352262
ISSN: 0733-950X
PURE UUID: 664c8d47-7fbd-4ce2-9349-16878df876ed

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Date deposited: 08 May 2013 10:38
Last modified: 18 Jul 2017 04:15

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Contributors

Author: Samir Gharbi
Author: Taha B. Ouarda
Author: Ousmane Seidou

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