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Research on port container throughput forecasting based on secondary decomposition and LSTM optimized by algorithm
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Bowen WANG, Yi HUANG, Xuanbo MENG, Tianyue CAO
Navigation of China | 2025, 48(4) : 121 - 131
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Navigation of China | 2025, 48(4): 121-131
Port and Waterway Engineering
Research on port container throughput forecasting based on secondary decomposition and LSTM optimized by algorithm
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Bowen WANG, Yi HUANG, Xuanbo MENG, Tianyue CAO
Affiliations
  • Shanghai Maritime Surveying and Mapping Center, Eastern Navigation Service Center, Maritime Safety Administration, Shanghai 200090, China
Published: 2025-12-25 doi: 10.3969/j.issn.1000-4653.2025.04.014
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Accurate forecasting of port container throughput is of great significance for port operators and government administrations in making scientific decisions. Existing forecasting methods, however, often pay insufficient attention to short-calendar-time PCT and exhibit limited accuracy in handling nonlinear and non-stationary fluctuation series. This paper takes the container throughput of Shanghai Port as the research object and proposes a novel deep learning model based on secondary decomposition using CCVMD and STL. Using the correlation coefficient as a reference, variational mode decomposition is first applied to the original time series. Subsequently, a secondary decomposition divides the data into seasonal, trend, and residual components. An algorithm-optimized long short-term memory neural network is then employed to predict each component separately, and the final prediction results are aggregated. Experimental results show that the combined decomposition model with data preprocessing significantly outperforms other models in PCT forecasting. The proposed model achieves a mean absolute percentage error of 0.021 703, a root mean square error percentage of 0.026 852, and a mean absolute error percentage of 0.022 14, indicating superior overall performance compared to 12 benchmark models and several models from prior studies. Furthermore, the secondary decomposition approach demonstrates enhanced reliability in tracking extreme values, removing and reducing noise, and improving interpretability.

container throughput forecasting  /  secondary decomposition  /  CCVMD  /  STL  /  HHO  /  LSTM  /  deep learning
Bowen WANG, Yi HUANG, Xuanbo MENG, Tianyue CAO. Research on port container throughput forecasting based on secondary decomposition and LSTM optimized by algorithm[J]. Navigation of China, 2025 , 48 (4) : 121 -131 . DOI: 10.3969/j.issn.1000-4653.2025.04.014
Year 2025 volume 48 Issue 4
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doi: 10.3969/j.issn.1000-4653.2025.04.014
  • Receive Date:2024-12-21
  • Online Date:2026-03-17
  • Published:2025-12-25
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  • Received:2024-12-21
Affiliations
    Shanghai Maritime Surveying and Mapping Center, Eastern Navigation Service Center, Maritime Safety Administration, Shanghai 200090, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
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占总种数比例
Percentage of
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种数
Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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