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Improved model of deep-draft ship squat in shallow waterways using stepwise regression trees

Improved model of deep-draft ship squat in shallow waterways using stepwise regression trees
Improved model of deep-draft ship squat in shallow waterways using stepwise regression trees
To maintain an optimum balance between security and efficiency of maritime transport in shallow waterways with a lot of deep-draft ship traffic such as in the St. Lawrence Waterway, it is particularly important to accurately estimate the ship squat, which is the reduction of the underkeel clearance between a vessel at rest and in motion. Recently, a squat model based on a regression tree was developed. The skill of this model to predict squat in the St. Lawrence Waterway exceeded the performance of 10 empirical models commonly used by the operational and regularity agencies. Although this approach is promising, two main problems were noticed: (1) the predictions obtained by the regression tree are not smooth and (2) the squat predicted with this model is not always monotonically increasing with ship speed (Froude number). In this paper, a stepwise regression tree algorithm is used to model squat. This approach has the same advantages as the regression tree (allowing the representation of complex and nonlinear relationships) and solves both of the aforementioned problems. Furthermore, the squat predictions of the new stepwise regression model outperform the predictions of the regression tree model and the Eryuzlu model, which is currently used by the Canadian Coast Guard. This new model could provide a handy tool for mariners to get real-time squat predictions in the St. Lawrence River. We also provide an algorithm that can be used to fit a squat model for any other economically important shallow waterway.
ship motion, waterways, regression models, statistics, st. lawrence river, shallow water
0733-950X
115-121
Beaulieu, Claudie
13ae2c11-ebfe-48d9-bda9-122cd013c021
Gharbi, Samir
9fb7df82-6c9d-4992-a0b1-65de75042622
Ouarda, Taha B.M.J.
33662875-c39e-42e9-8b21-9b1452d5d596
Charron, Christian
5d7d89d8-7acf-4ae1-98ab-1a2e51a0e76e
Aissia, Mohamed Aymen Ben
68d79747-5305-4515-9e49-fcdc3cd0e0c1
Beaulieu, Claudie
13ae2c11-ebfe-48d9-bda9-122cd013c021
Gharbi, Samir
9fb7df82-6c9d-4992-a0b1-65de75042622
Ouarda, Taha B.M.J.
33662875-c39e-42e9-8b21-9b1452d5d596
Charron, Christian
5d7d89d8-7acf-4ae1-98ab-1a2e51a0e76e
Aissia, Mohamed Aymen Ben
68d79747-5305-4515-9e49-fcdc3cd0e0c1

Beaulieu, Claudie, Gharbi, Samir, Ouarda, Taha B.M.J., Charron, Christian and Aissia, Mohamed Aymen Ben (2012) Improved model of deep-draft ship squat in shallow waterways using stepwise regression trees. Journal of Waterway, Port, Coastal, and Ocean Engineering, 138 (2), 115-121. (doi:10.1061/(ASCE)WW.1943-5460.0000112).

Record type: Article

Abstract

To maintain an optimum balance between security and efficiency of maritime transport in shallow waterways with a lot of deep-draft ship traffic such as in the St. Lawrence Waterway, it is particularly important to accurately estimate the ship squat, which is the reduction of the underkeel clearance between a vessel at rest and in motion. Recently, a squat model based on a regression tree was developed. The skill of this model to predict squat in the St. Lawrence Waterway exceeded the performance of 10 empirical models commonly used by the operational and regularity agencies. Although this approach is promising, two main problems were noticed: (1) the predictions obtained by the regression tree are not smooth and (2) the squat predicted with this model is not always monotonically increasing with ship speed (Froude number). In this paper, a stepwise regression tree algorithm is used to model squat. This approach has the same advantages as the regression tree (allowing the representation of complex and nonlinear relationships) and solves both of the aforementioned problems. Furthermore, the squat predictions of the new stepwise regression model outperform the predictions of the regression tree model and the Eryuzlu model, which is currently used by the Canadian Coast Guard. This new model could provide a handy tool for mariners to get real-time squat predictions in the St. Lawrence River. We also provide an algorithm that can be used to fit a squat model for any other economically important shallow waterway.

Full text not available from this repository.

More information

e-pub ahead of print date: 14 July 2011
Published date: March 2012
Keywords: ship motion, waterways, regression models, statistics, st. lawrence river, shallow water
Organisations: Ocean and Earth Science

Identifiers

Local EPrints ID: 352249
URI: https://eprints.soton.ac.uk/id/eprint/352249
ISSN: 0733-950X
PURE UUID: 4305e573-887c-4a3b-bcac-e104c8761ced

Catalogue record

Date deposited: 08 May 2013 09:12
Last modified: 18 Jul 2017 04:16

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