Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

A Hybrid SVR-BP Model for Resistance Prediction of the Container Ship at Outside Drafts
Yunfei Yang1, Chen Li1, Jianglong Sun1,2,3,*
1. School of Naval Architecture and Ocean Engineering (HUST), Wuhan, Hubei, China
2. Hubei Provincial Engineering Research Center of Data Techniques and Supporting Software for Ships (DTSSS), Wuhan, Hubei, China
3. Collaboration Innovation Center for Advanced Ship and Deep-sea Exploration (CISSE), Shanghai, China

ABSTRACTalgorithms in ship resistance prediction remains at the stage of using
resistance maps for interpolation simply. The research on theapplication of AI algorithms in ship resistance prediction is not enough,especially for the resistance prediction at the drafts outside of training
set for the same ship.

The accurate prediction of ship resistance has great significance.
Generally, resistance prediction accuracy at the drafts outside oftraining set is poor compared with that at the drafts inside of training
set. In this paper, a prediction method for the resistance at outside drafts
is proposed using the hybrid support vector regression - backpropagation (SVR-BP) model. Ship resistance is predicted by SVR
firstly, and BP is used to make for the errors between SVR prediction
results and experimental values. The study shows that the prediction
accuracy using the SVR-BP model is better than using the single SVR
or BP model.

The purpose of this research is to establish a prediction model for ship
resistance at outside drafts using the ship’s features and resistance data.
Ship resistance is affected by many factors, such as ship dimensions
and hull shape, etc. The relationship between ship resistance at outside
drafts and these factors is nonlinear and complex. It is different fromthe resistance prediction at the drafts inside of training set, which canbe obtained accurately using single machine learning models (Yang etal., 2020).

KEY WORDS: ship resistance prediction; support vector regression;back propagation; hybrid machine learning model.

In this paper, a hybrid SVR-BP model is used to establish a resistanceprediction model for drafts outside the training set. A 13500TEU
container ship is taken as the research object, and the single SVR andBP models are taken as comparisons. The towing tank test of13500TEU container ship is used to get training data and verify theprediction results.

INTRODUCTION
Resistance prediction has always been one of the hot regions of ship
research. Approximate methods such as series data, empirical formula,and parent ship estimation are usually used to predict ship resistance.
Some methods are modified for different ship types and working
conditions (Jeong et al., 2017; Liu et al., 2014; Kristensen, 2013; Tu etal., 2018). With the development of computer technology, CFD
technology has been widely used in ship performance calculation (Guoet al., 2013; Li et al., 2014; Sun et al., 2016; Tu et al., 2018; Lv et al.,2013; Lyu et al., 2018). Compared with the traditional resistanceprediction methods, CFD technology has higher accuracy. Both ofthese prediction methods have shortcomings. The prediction accuracyof approximate methods needs to be improved, and the CFD
technology needs more time and requires high computational resources.

The work of this paper is presented as follows. 2nd section briefly
defines the BP, SVR, and SVR-BP models. 3rd section introduces themodel ship features and resistance of the 13500TEU container ship. 4thsection presents the experiment processes of machine learningalgorithms. The processes include data set division, prediction scheme,
model parameters selection, and the evaluation metric of prediction
models. 5th section provides the predicted results of resistance atoutside drafts using different prediction models and the comparison ofresults. 6th section gives conclusions at last.

MACHINE LEARNING ALGORITHMS

Artificial intelligence (AI) algorithms such as machine learning anddeep learning have a strong ability to deal with nonlinear and complex
problems and are making marks in the regions of image recognition andspeech synthesis. These algorithms are also increasingly used in thestudy of shipping (Atmane Khellal et al., 2018; Niu et al.,2017; Guan etal., 2018) and fluid mechanics (Ling et al.,2016; Wang et al., 2017;
Rabault et al., 2019; Raissi et al., 2018). The current application of AI

BP Neural Network
BP neural network (Rumelhart et al., 1986) is one of the most widelyused artificial neural network models. A typical BP neural network
comprises three layers, an input layer, a hidden layer, and an output

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