Ship trajectory planning for collision avoidance using hybrid ARIMA-LSTM models

Abebe, Misganaw; Noh, Yoojeong; Kang, Young-Jin; Seo, Chanhee; Kim, Dong-Hyun; Seo, Jin Joo

doi:10.1016/j.oceaneng.2022.111527

Cited by 53 publications

(24 citation statements)

References 28 publications

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“…Where, 𝑦 𝑡 is the number of difference levels, 𝑐is a constant value, 𝜙 is the AR parameter (autocorrelation size), 𝑝 is the number of lags (AR order), 𝜃 is the MA parameter value (error autocorrelation), 𝑞 denotes the number of lags (order of the model MA), and 𝑒 𝑡 is the error [24]。 𝑘 is the number of model parameters, 𝑛 is the number of samples, and 𝐿 is the likelihood function.…”

Section: Methodsmentioning

confidence: 99%

Section: 𝐸 (𝑦(𝑡mentioning

confidence: 99%

“…Since linear and nonlinear modeling methods have their own characteristics, the former can only identify linear features of time series, while the latter can effectively mine them[29]. The ARIMA model can predict shortperiod linear trends well, while the LSTM model can predict complex, non-linear time series well[24].The ARIMA model is used to predict the linear and nonlinear components of the data, which is then fed into the deep neural network and fit to obtain the predicted value of the nonlinear component. On this basis, the data of both linear and nonlinear aspects are integrated, and the final prediction result is obtained.…”

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confidence: 99%

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Air-Quality Prediction Based on the ARIMA-CNN- LSTM Combination Model optimized by Dung Beetle Optimizer

Duan

Gong

Luo

et al. 2023

Preprint

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The air pollution problem seriously affects economic development and people's health, and an efficient and accurate forecasting model of air quality will help manage air pollution problems. The comparison models chosen by other scholars are often based on derivative models of the proposed model and do not comprehensively compare other types of models with limited accuracy. In this paper, we establishes a combined ARIMA-CNN-LSTM model to predict the air quality index accurately. The model mainly consists of two parts: Using the ARIMA model to fit the linear part of the data and using the CNN-LSTM model to fit the nonlinear part of the data to avoid the problem of blindness in the CNN-LSTM hyperparameter setting. To avoid the dilemma of blindness in a CNN-LSTM hyperparameter setting, this article uses the Dung Beetle Optimizer tool to find the hyperparameters of a CNN-LSTM model, determine the best hyperparameters, and check the accuracy of the model. The proposed model is compared with other widely used models. The results show that the Dung Beetle Optimizer can effectively search for the optimal hyperparameters of the model and can solve the problem of blindness in setting the hyperparameters of the model. And the optimized ARIMA-DBO-CNN-LSTM model has higher predictive accuracy with stronger adaptability in predicting the three cities.

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Section: Methodsmentioning

confidence: 99%

Section: 𝐸 (𝑦(𝑡mentioning

confidence: 99%

mentioning

confidence: 99%

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Air-Quality Prediction Based on the ARIMA-CNN- LSTM Combination Model optimized by Dung Beetle Optimizer

Duan

Gong

Luo

et al. 2023

Preprint

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show abstract

“…Combining the characteristics of the autoregressive model and the MA model, an Autoregressive Moving Average (ARMA) model that takes into account past time and external influences can be obtained. The mathematical model is [9]:…”

Section: Arima Prediction Modelmentioning

confidence: 99%

ARIMA assisted low-cost MEMS-INS/GNSS integrated navigation in GNSS denial environment

Li,

Chai

2024

Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023)

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Global Navigation Satellite System (GNSS) and low-cost Inertial Navigation System (INS) is commonly used in fields such as vehicle navigation. However, when the carrier is in a GNSS rejection environment, the navigation accuracy rapidly decreases and cannot meet the positioning accuracy requirements. Therefore, this paper uses the Autoregressive Integrated Moving Average model (ARIMA) to learn the positioning information of GNSS during normal operation, output predictive values in the case of GNSS unavailable, and fuse them with the MEMS-INS mechanical arrangement results through Kalman filtering. Vehicle experiments show that the proposed algorithm significantly improves the navigation performance compared to traditional GNSS/INS integrated navigation, reducing the maximum position error in the east and north directions by 76.4% and 69.2%, respectively, and increasing the root mean square error of position accuracy in the east, north, and up directions by an order of magnitude.

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“…Hu T developed an adaptive time delay compensation algorithm based on thorough analysis of the integrated information transmission platform and the composition and characteristics of the time delay in channel, during which forward and reverse transmission channels have been developed properly [30]. The neural network algorithm is gradually widely used in the field of ship motion signal prediction [31,32]. Kaklis D examines how a predictive FOC scheme can be coupled with WR optimization algorithms in order to reduce the vessel's FOC, emissions, and the overall cost of a voyage [33].…”

Section: Introductionmentioning

confidence: 99%

A Prediction Method of Ship Motion Based on LSTM Neural Network with Variable Step-Variable Sampling Frequency Characteristics

Han

Xiong

2023

JMSE

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In active heave compensation, in order to realize the smooth control of the heave compensation platform, it is necessary to use the ship motion measurement system to accurately obtain the ship displacement signal, invert the ship displacement signal, and then control the expansion and contraction of the electric cylinder so that the compensation platform remains horizontal. The ship displacement measurement system generally adopts the second integral of the acceleration sensor to obtain the ship displacement signal. During the acquisition process of the ship displacement signal, the quadratic integration process of the acceleration, and the communication process of the output control command, there is a processing lag which makes the error accumulate, resulting in a delay in the measurement of the ship motion. In order to collect the ship motion more accurately and control the heave compensation platform more precisely, this paper proposes a ship motion prediction method based on a variable step-variable sampling frequency characteristic LSTM (Long Short-Term Memory) neural network. First, we use the autocorrelation function algorithm to calculate the inherent delay of the lag in the process of signal acquisition by the measurement system. Secondly, the LSTM neural network is used to predict the inherent delay step of the lagging ship displacement signal. During the prediction process, it is found that the difference in the sampling frequency of the displacement signal will lead to a change in the step of the inherent delay—experiment in the laboratory to verify. By controlling the motion platform to simulate the motion of the ship and using the ship motion measurement system and the laser sensor system to measure the displacement signal of the motion platform synchronously, it is verified that the ship motion measurement system does have an inherent delay. Thirdly, on a sailing ship, ship displacement signals are collected by setting multiple sets of ship motion measurement systems. Finally, multiple sets of sampling frequency and multiple steps are set, and the ship motion is predicted based on the variable step-variable sampling frequency LSTM neural network. It is verified that the prediction accuracy is related to the sampling frequency of the signal collector and the prediction step of the LSTM neural network, which improves the prediction accuracy of the model and the timeliness of ship motion acquisition.

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Ship trajectory planning for collision avoidance using hybrid ARIMA-LSTM models

Cited by 53 publications

References 28 publications

Air-Quality Prediction Based on the ARIMA-CNN- LSTM Combination Model optimized by Dung Beetle Optimizer

Air-Quality Prediction Based on the ARIMA-CNN- LSTM Combination Model optimized by Dung Beetle Optimizer

ARIMA assisted low-cost MEMS-INS/GNSS integrated navigation in GNSS denial environment

A Prediction Method of Ship Motion Based on LSTM Neural Network with Variable Step-Variable Sampling Frequency Characteristics

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