Application of ensemble learning models on ship radiation noise prediction

QIANG Yiming; CHEN Yihong; PEI Yuqing; PANG Yezhen; XIE Shuo

doi:10.11993/j.issn.2096-3920.2025-0111

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QIANG Yiming, CHEN Yihong, PEI Yuqing, PANG Yezhen, XIE Shuo. Application of ensemble learning models on ship radiation noise prediction[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0111

Citation:

QIANG Yiming, CHEN Yihong, PEI Yuqing, PANG Yezhen, XIE Shuo. Application of ensemble learning models on ship radiation noise prediction[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0111

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Application of ensemble learning models on ship radiation noise prediction

doi: 10.11993/j.issn.2096-3920.2025-0111

QIANG Yiming^{1, 3},
CHEN Yihong^{1, 2
,},
PEI Yuqing^{1, 2},
PANG Yezhen^{1, 2},
XIE Shuo¹

1.
China Ship Scientific Research Center, Wuxi 214082, China
2.
Ship Vibration and Noise Key Laboratory, Wuxi 214082, China
3.
School of Ocean and Civil Engineering, Shanghai Jiaotong University, Shanghai 200240, China

Received Date: 2025-08-22
Accepted Date: 2025-09-15
Rev Recd Date: 2025-09-12

Available Online: 2025-11-26

Abstract

Abstract

Ship radiation noise induced by vibration is a major concern for the comfort and stealthy of commercial ships and warships. An accurate prediction for the distribution and sound level of ship radiation noise can assist effective ship designs to lower vibration and noise. In order to avoid constructing complex functions from multiple vibration sources, this paper used Random Forest(RF) and Gradient Boosting Decision Tree(GBDT) methods to build surrogate models to quickly predict the 1/3 rd octave noise level of different working conditions and measuring points. Ensemble learning models have a better performance on 3 out of 4 conditions compared to Bayesian Regression, with MAE prediction error less than 5 dB. This paper also proposed an optimized version of above models that combines ensemble learning methods and linear regression, which increased prediction accuracy by 1.5 dB. The proposed ensemble learning methods could be an efficient tool for ship radiation noise analysis.
- ensemble learning,
- ship radiated noise,
- surrogate model,
- vibrate

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References(19)

References

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