Advancements in Deep Learning Techniques for Time Series Forecasting in Maritime Applications: A Comprehensive Review
Abstract
:1. Introduction
2. Literature Collection Procedure
- Search scope: Titles, Keywords, and Abstracts
- Keywords 1: ‘deep’ AND ‘learning’, AND
- Keywords 2: ‘time AND series’, AND
- Keywords 3: ‘maritime’, OR
- Keywords 4: ‘vessel’, OR
- Keywords 5: ‘shipping’, OR
- Keywords 6: ‘marine’, OR
- Keywords 7: ‘ship’, OR
- Keywords 8: ‘port’, OR
- Keywords 9: ‘terminal’
- Retain only articles related to maritime operations. For example, studies on ship-surrounding weather and risk prediction based on ship data will be kept, while research solely focused on marine weather or wave prediction that is unrelated to any aspect of maritime operations will be excluded.
- Exclude neural network studies that do not employ deep learning techniques, such as ANN or MLP with only one hidden layer.
- The language of the publications must be English.
- The original data used in the papers must include time series sequences.
3. Deep Learning Algorithms
3.1. Artificial Neural Network (ANN)
3.1.1. Multilayer Perceptron (MLP)/Deep Neural Networks (DNN)
3.1.2. WaveNet
3.1.3. Randomized Neural Network
3.2. Convolutional Neural Network (CNN)
3.3. Recurrent Neural Network (RNN)
3.3.1. Long Short-Term Memory (LSTM)
3.3.2. Gated Recurrent Unit (GRU)
3.4. Attention Mechanism (AM)/Transformer
3.5. Overview of Algorithms Usage
4. Time Series Forecasting in Maritime Applications
4.1. Ship Operation-Related Applications
4.1.1. Ship Trajectory Prediction
- A.
- Navigation Safety Enhancement
- B.
- Ship Anomaly Detection
- C.
- Intelligent Navigation Practice
4.1.2. Meteorological Factor Prediction
4.1.3. Ship Fuel Consumption Prediction
4.1.4. Others
4.2. Port Operation-Related Applications
4.3. Shipping Market-Related Applications
4.4. Overview of Time Series Forecasting in Maritime Applications
5. Overall Analysis
5.1. Literature Description
5.1.1. Literature Distribution
5.1.2. Literature Classification
5.2. Data Utilized in Maritime Research
5.2.1. Automatic Identification System Data (AIS Data)
5.2.2. High-Frequency Radar Data and Sensor Data
5.2.3. Container Throughput Data
5.2.4. Other Datasets
5.3. Evaluation Parameters
5.4. Real-World Application Examples
5.5. Future Research Directions
5.5.1. Data Processing and Feature Extraction
5.5.2. Model Optimization and Application of New Technologies
5.5.3. Specific Application Scenarios
5.5.4. Practical Applications and Long-Term Predictions
5.5.5. Environmental Impact, Fault Prediction, and Cross-Domain Applications
6. Conclusions
- (1)
- This study fills the gap in the literature on advancements in deep learning techniques for time series forecasting in maritime applications, focusing on three key areas: ship operations, port operations, and shipping markets.
- (2)
- Different types of deep learning models are compared to identify the most suitable models for various applications. The differences between these models are discussed, providing valuable insights for domain researchers.
- (3)
- The study summarizes future research directions, helping to clarify future research objectives and guide subsequent studies. These directions include enhancing data processing and feature extraction, optimizing deep learning models, and exploring specific application scenarios. Additionally, practical applications and long-term predictions, environmental impacts, fault prediction, and cross-domain applications are addressed to provide comprehensive guidance for future research efforts.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Ref. | Architecture | Dataset | Advantage |
---|---|---|---|
[64] | MSCNN-GRU-AM | HF radar | It is applicable for high-frequency radar ship track prediction in environments with significant clutter and interference |
[80] | CNN-BiLSTM-Attention | 6L34DF dual fuel diesel engine | The high prediction accuracy and early warning timeliness can provide interpretable fault prediction results |
[122] | LSTM | Two LNG carriers | Enables early anomaly detection in new ships and new equipment |
[98] | LSTM | sensors | better and high-precision effects |
[42] | Self-Attention-BiLSTM | A real military ship | Not only can it better capture complex ship attitude changes, but it also shows greater accuracy and stability in long-term forecasting tasks |
[41] | CNN–GRU–AM | A C11 containership | better accuracy of forecasting |
[121] | GRU | A scaled model test | good prediction accuracy |
[123] | CNN | A bulk carrier | good prediction accuracy |
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Wang, M.; Guo, X.; She, Y.; Zhou, Y.; Liang, M.; Chen, Z.S. Advancements in Deep Learning Techniques for Time Series Forecasting in Maritime Applications: A Comprehensive Review. Information 2024, 15, 507. https://doi.org/10.3390/info15080507
Wang M, Guo X, She Y, Zhou Y, Liang M, Chen ZS. Advancements in Deep Learning Techniques for Time Series Forecasting in Maritime Applications: A Comprehensive Review. Information. 2024; 15(8):507. https://doi.org/10.3390/info15080507
Chicago/Turabian StyleWang, Meng, Xinyan Guo, Yanling She, Yang Zhou, Maohan Liang, and Zhong Shuo Chen. 2024. "Advancements in Deep Learning Techniques for Time Series Forecasting in Maritime Applications: A Comprehensive Review" Information 15, no. 8: 507. https://doi.org/10.3390/info15080507