Prediction of Significant Wave Heights Based on CS-BP Model in the South China Sea

Yang, Shaobo; Xia, Tianliang; Zhang, Zhenquan; Zheng, Chunmiao; Li, Xingfei; Li, Hongyu; Xu, Jianjun

doi:10.1109/access.2019.2944422

Cited by 13 publications

(5 citation statements)

References 36 publications

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“…Malekmohamadi et al [7] evaluated SVM, Bayesian Network, ANN, and Adaptive Neuro-Fuzzy Inference System for SWH prediction and found that ANN achieved the best performance. In recent years, several variances of ANN, such as General Regression Neural Network [12], and various optimizers of ANN, such as cuckoo search algorithm [13], mind evolutionary algorithm [14] have been investigated to improve the accuracy of SWH prediction.…”

Section: Related Work a Machine Learning-based Approachesmentioning

confidence: 99%

Significant Wave Height Prediction based on Wavelet Graph Neural Network

Chen

Liu

Zhang

et al. 2021

Preprint

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Computational intelligence-based ocean characteristics forecasting applications, such as Significant Wave Height (SWH) prediction, are crucial for avoiding social and economic loss in coastal cities. Compared to the traditional empiricalbased or numerical-based forecasting models, "soft computing" approaches, including machine learning and deep learning models, have shown numerous success in recent years. In this paper, we focus on enabling the deep learning model to learn both shortterm and long-term spatial-temporal dependencies for SWH prediction. A Wavelet Graph Neural Network (WGNN) approach is proposed to integrate the advantages of wavelet transform and graph neural network. Several parallel graph neural networks are separately trained on wavelet decomposed data, and the reconstruction of each model's prediction forms the final SWH prediction. Experimental results show that the proposed WGNN approach outperforms other models, including the numerical models, the machine learning models, and several deep learning models.

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Section: Related Work a Machine Learning-based Approachesmentioning

confidence: 99%

Significant Wave Height Prediction based on Wavelet Graph Neural Network

Chen

Liu

Zhang

et al. 2021

Preprint

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“…The BP networks have strong learning ability, and also used for fuel consumption prediction [25]- [26]. Elman neural network [27] is a typical dynamic recurrent neural network, which is generally composed of four layers: input layer, hidden layer, context layer and output layer. The input layer unit only plays the role of signal transmission, and the output layer unit plays the role of weighting, the hidden layer unit has two types of linear and nonlinear excitation functions.…”

Section: Model Buildingmentioning

confidence: 99%

Fitting Analysis of Inland Ship Fuel Consumption Considering Navigation Status and Environmental Factors

Zhi

Liu

et al. 2020

IEEE Access

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The strategy of ecological priority and green development in China has made the fuel consumption of inland ships receive unprecedented attentions. Reliable fuel consumption prediction is the vital basis of navigation planning, energy supervision, and efficiency optimization. In this paper, a cargo ship sailing on the Yangtze River trunk line was taken as the research object. A comprehensive fitting analysis of inland ship fuel consumption was conducted, and a prediction method was proposed. First, the multi-source data including ship navigation status and environment information were collected by multi-source sensors. Second, to conduct a detailed analysis of the collected data, the authors proposed data pre-processing and trajectory segmentation methods and analyzed the correlation between multi-source variables and fuel consumption. Third, a Back Propagation Neural Network with double hidden layers (DBPNN) was tailored to build a fuel consumption prediction model. Fourth, the developed model was validated using real ship measurement data. Different input variables were selected for fuel consumption prediction, and the results showed that after adding the variables of environmental feature including water level, water speed, wind speed, wind angle, and route segment, the prediction error RMSE (root mean square error) and MAE (mean absolute error) were reduced by 35.31% and 30.30%, respectively, while the R 2 (R-squared) increased to 0.9843. What's more, compared with other ANNs (artificial neural networks) such as Elman, RBF (radial basis function), three support vector regression (SVR) models, random forest regression (RFR) model, GRNN (generalized regression neural network), RNN (recurrent neural network), GRU (gated recurrent unit) and LSTM (long short-term memory) the proposed DBPNN model showed better performance in fuel consumption prediction.

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“…In designing marine structures such as platforms, breakwaters, and jetties, the main parameter in determining their various components' stability and design is the wave height in the region [3,4]. When waves approach coastal areas, they are deformed due to various phenomena such as shallow, scattering, refraction, and refection, which are important in various aspects such as management, protection, and exploitation of the coast, environment, fsheries, navigation, and construction of structures [5][6][7]. Te study of sea wave's ofshore and onshore structures develops basic knowledge in the feld of coastal engineering and the physics of the sea and waves.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height

Chen

Rashid

Sayed

et al. 2023

Advances in Civil Engineering

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In coastal and port engineering, wind-generated waves have always been a crucial, fundamental, and important topic. As a result, various methods for estimating wave parameters, including field measurement and numerical methods, have been proposed over time. This study evaluates the wave height at Sri-Lanka Hambantota Port using soft computing models such as Artificial Neural Networks (ANNs) and the M5 model tree (M5MT). In order to overcome its nonstationarity, the primary wave height time series were divided into subtime series using the wavelet transform. The collected subtime series were then utilized as input data for ANN and M5MT in order to determine the wave height. For the sake of the model performance, the daily wind and wave data from the Acoustic Wave and Current (AWAC) sensor for Hambantota Port in 2020 and Sanmen Bay in 2017 were used in this study. The training state utilizes 80% of the available data, while the test state uses 20%. The Root Mean Square Error (RMSE) of the ANN, M5, WANN, and Wavelet-M5 models in the Hambantota Port for the test stage are 0.12, 0.11, 0.04, and 0.06, respectively. While in Sanmen Bay, the RMSE of the ANN, M5, WANN, and Wavelet-M5 models for the test stage are 0.14, 0.16, 0.06, and 0.08, respectively. According to the findings of this study, the accuracy of WANN and Wavelet-M5 hybrid models in evaluating wave height is superior to that of classic ANN and M5MT, and it is recommended that WANN and Wavelet-M5 hybrid models be used to estimate wave height.

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Prediction of Significant Wave Heights Based on CS-BP Model in the South China Sea

Cited by 13 publications

References 36 publications

Significant Wave Height Prediction based on Wavelet Graph Neural Network

Significant Wave Height Prediction based on Wavelet Graph Neural Network

Fitting Analysis of Inland Ship Fuel Consumption Considering Navigation Status and Environmental Factors

Evaluation of Hybrid Soft Computing Model’s Performance in Estimating Wave Height

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