Identifying ship-wakes in a shallow estuary using machine learning. (15th February 2022)

• Record Type: Journal Article
• Authors: Luo, Yao; Zhang, Cheng; Liu, Junliang; Xing, Huanlin; Zhou, Fenghua; Wang, Dongxiao; Long, Xiaomin; Wang, Shengan; Wang, Weiqiang; Shi, Fengyan
• Is Part Of: Ocean engineering. Volume 246(2022)
• Publication Date: 2022-02-15
• Subjects: Ship-wakes -- Machine learning -- Shallow estuary -- In situ wave observation -- Pearl River Estuary; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2021.110456
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: Ship wakes generated in relatively shallow estuaries are subjected to complex dynamic processes such as nonlinear wave evolution and wave–wave interaction. Identifying ship-wakes in coastal waters is important for maritime and coastal management, including waterway planning, shoreline protection, and navigation-related problems in busy waters. In this study, a machine learning framework was developed to identify ship-wakes using supervised training algorithms. Wave data were measured at two stations in the Pearl River Estuary of South China. The two stations are 1, 500 m apart, one is at near-field of the navigation channel, and the other is at far-field. Two machine learning techniques, namely, the multilayer perceptron (MLP) model and support vector machine (SVM) model, are employed. We used two data formats in the training and testing processes; one is the digital data of wave time series, the other is the spectrogram derived from the time series. Tests suggested that spectrogram is a more appropriate format for both models versus the time-series format. The SVM model has higher cross-validation scores and higher computational costs compared to the MLP model. The trained models using the near-field data can be used for predictions at the far-field location with high accuracy. Sensitivity tests revealed that long primary waves and primary wake chirps are the most critical components in the spectrogram among overall ship-wake characteristics for ship-wake recognition. The time and frequency resolutions in the spectrogram only have minor effects on model performance. Highlights: Machine learning techniques are used to recognize ship-wakes from wave data. Spectrogram of ship-wakes are applied as input items in the machine learning models. Both the MLP and SVM models are able to identify ship-wakes with high accuracy.