An EMD-SVR model for short-term prediction of ship motion using mirror symmetry and SVR algorithms to eliminate EMD boundary effect

Nie, Zhihong; Shen, Feng; Xu, Dingjie; Li, Qinhua

doi:10.1016/j.oceaneng.2020.107927

Cited by 52 publications

(12 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e traditional regression methods consider the prediction correct if and only when the regression fitting function is completely equal to y . For example, (f(x) − y) 2 is commonly used to calculate the loss in linear regression [51]. However, SVR believes that as long as f(x) deviates from y not too much, the prediction can be considered correct.…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Zhang

et al. 2020

Advances in Civil Engineering

View full text Add to dashboard Cite

The existing pavement performance prediction methods are limited to single-factor predictions, which often face the challenges of high cost, low efficiency, and poor accuracy. It is difficult to simultaneously solve the temporal, spatial, and exogenous dependencies between pavement performance data and maintenance, the service life of highways, the environment, and other factors. Digital twin technology based on the building information modeling (BIM) model, combined with machine learning, puts forward a new perspective and method for the accurate and timely prediction of pavement performance. In this paper, we propose a highway tunnel pavement performance prediction approach based on a digital twin and multiple time series stacking (MTSS). This paper (1) establishes an MTSS prediction model with heterogeneous stacking of eXtreme gradient boosting (XGBoost), the artificial neural network (ANN), random forest (RF), ridge regression, and support vector regression (SVR) component learners after exploratory data analysis (EDA); (2) proposes a method based on multiple time series feature extraction to accurately predict the pavement performance change trend, using the highway segment as the minimum computing unit and considering multiple factors; (3) uses grid search with the k-fold cross validation method to optimize hyperparameters to ensure the robustness, stability, and generalization ability of the prediction model; and (4) constructs a digital twin for pavement performance prediction to realize the real-time dynamic evolution of prediction. The method proposed in this study is applied in the life cycle management of the Dalian highway-crossing tunnel in Shanghai, China. A dataset covering 2010–2019 is collected for real-time prediction of the pavement performance. The prediction accuracy evaluation shows that the mean absolute error (MAE) is 0.1314, the root mean squared error (RMSE) is 0.0386, the mean absolute percentage error (MAPE) is 5.10%, and the accuracy is 94.90%. Its overall performance is better than a single model. The results verify that the prediction method based on digital twin and MTSS is feasible and effective in the highway tunnel pavement performance prediction.

show abstract

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Zhang

et al. 2020

Advances in Civil Engineering

View full text Add to dashboard Cite

show abstract

“…The third way is not friendly to real-time prediction, because it allocates independent parallel neural networks to each sub-series and then fully connects them, leading to considerable extra complexity. Besides WT, there are other methods of signal decomposition, such as EMD [46], but they have shown neither better interpretability nor better performance than WT, so we don't consider them in this paper.…”

Section: ) Non-nn Prediction Modelsmentioning

confidence: 99%

Modeling of Coupling Effects in Neural Networks for Ship Motion Prediction

Zhang¹,

Hu²,

Zhang³

et al. 2022

Preprint

View full text Add to dashboard Cite

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.<div><br></div><div>A major revision is done, compared to the previous version.</div><div><br></div><div>Abstract: Neural networks (NNs) have been applied to predict complex ship motions for stable ship maneuvering and operations at sea, but the coupling effects of different DoFs are still not effectively modeled in black-box NNs. To this end, we propose a novel deep NN framework that explicitly models the coupling effects through factorization machines, and significantly improves the prediction accuracy without introducing much extra complexity. The proposed framework is also lightweight and frequency-aware, which serves real-time and high-frequency prediction with high accuracy. We experimentally demonstrated that the framework is not only friendly to real-time prediction and online learning, but also interpretable and with great potential to be complemented by common machine learning tricks and go deeper.<br></div><div><br></div>

show abstract

“…Relationship between wave height and vessel motion is in Eq. (11). The heave motion can be simplifying: where 2v zz is damping coefficient of heave motion.…”

Section: B Vessel Motion Modelmentioning

confidence: 99%

“…The data-driven methods learn patterns from historical observations and further use the learned patterns to predict future vessels motion. The common strategies are real-time prediction [8]- [10], and short-term prediction [11]. The machine learning-based prediction models were used for the real-time vessels motion prediction to their capability in nonlinearity processing.…”

Section: Introductionmentioning

confidence: 99%

“…With the advantage of the networks remembering inputs for a long time and an explicit memory, LSTM has been widely applied in many areas, especially in time series modeling, like predictions of sea waves [30] and vessel motion [11]. In this paper, a short-term prediction study about the vessel heave motion based on an improved LSTM model with dropout has been proposed in marine operations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Short-Term Prediction in Vessel Heave Motion Based on Improved LSTM Model

et al. 2021

View full text Add to dashboard Cite

In order to solve the problem of control performance degradation caused by time delay in wave compensation control system, predicting vessel heave motion can be the input vector of the control system to alleviate time delay problem. The vessel heave motion belongs to the problem of time series, this paper proposes an improved Long Short-Term Memory (LSTM) model with a random deactivation layer (dropout), which can deal with the time series problem very well. In order to obtain the vessel heave motion, this paper establishes a wave model suitable for marine operation, and solves the vessel heave motion through the mathematical model of vessel motion. Finally, the paper predicts the vessel heave motion in a short predicted time series. In the process of obtaining the prediction effect of vessel heave motion, the Back Propagation (BP) neural network and the standard LSTM neural network are used to compare with the improved LSTM neural network. While the predicted time series is 0.1 s at sea state 3, the mean absolute percentage (MAPE) errors of BP neural network in the prediction of vessel heave motion is 1.06 × 10 −2 %, the standard LSTM in the prediction of heave motion is 1.43×10 −4 %, the improved LSTM in the prediction of heave motion is 7.51 × 10 −6 %. The improved LSTM improves MAPE by 1.05 × 10 −2 % compared with the BP and 1.42 × 10 −4 % compared with the standard LSTM. The prediction results show that the improved LSTM has a strong prediction capability with not easily overfitted in vessel heave motion prediction. The results show that the improved LSTM provides a new idea for vessel motion prediction and solves the problem of time delay, which is useful for the study of stability in marine operations.INDEX TERMS Short-term prediction, improved long short-term memory (LSTM), vessel heave motion, time delay.

show abstract

An EMD-SVR model for short-term prediction of ship motion using mirror symmetry and SVR algorithms to eliminate EMD boundary effect

Cited by 52 publications

References 19 publications

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Modeling of Coupling Effects in Neural Networks for Ship Motion Prediction

Short-Term Prediction in Vessel Heave Motion Based on Improved LSTM Model

Contact Info

Product

Resources

About