Real-time prediction of port water levels based on EMD-PSO-RBFNN
Addressing the spatial variability, temporal dynamics, and non-linearity characteristics of port water levels, a hybrid prediction scheme was proposed, which integrates empirical mode decomposition (EMD) with a radial basis function neural network (RBFNN), optimized using the particle swarm optimiza...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Marine Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2025.1537696/full |
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| _version_ | 1832591023429124096 |
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| author | Lijun Wang Shenghao Liao Sisi Wang Jianchuan Yin Ronghui Li Jingyu Guan |
| author_facet | Lijun Wang Shenghao Liao Sisi Wang Jianchuan Yin Ronghui Li Jingyu Guan |
| author_sort | Lijun Wang |
| collection | DOAJ |
| description | Addressing the spatial variability, temporal dynamics, and non-linearity characteristics of port water levels, a hybrid prediction scheme was proposed, which integrates empirical mode decomposition (EMD) with a radial basis function neural network (RBFNN), optimized using the particle swarm optimization (PSO) algorithm. First, through the application of EMD, the port water level time series was decomposed into sub-series characterized by lower non-linearity. Subsequently, PSO was applied to fine-tune the center and spread parameters of the RBFNN, thereby enhancing the model’s predictive performance. The optimized PSO-RBFNN model was employed to make predictions on the decomposed sub-series. Finally, reconstruction of the predicted sub-series yielded the final water level predictions. The feasibility and effectiveness of the proposed model were validated using measured port water level data. Results from simulations highlighted the model’s ability to deliver accurate predictions across various lead times. Furthermore, comparative analysis revealed that the proposed model outperforms alternative methods in port water level prediction. Therefore, the proposed model serves as a reliable, efficient, and real-time prediction tool, providing robust support for port operational safety. |
| format | Article |
| id | doaj-art-48d89c93e4f141bbb8bea0e8f4d027c8 |
| institution | Kabale University |
| issn | 2296-7745 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj-art-48d89c93e4f141bbb8bea0e8f4d027c82025-01-23T05:10:26ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452025-01-011210.3389/fmars.2025.15376961537696Real-time prediction of port water levels based on EMD-PSO-RBFNNLijun Wang0Shenghao Liao1Sisi Wang2Jianchuan Yin3Ronghui Li4Jingyu Guan5Naval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang, ChinaNaval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang, ChinaNaval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang, ChinaGuangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching, Guangdong Ocean University, Zhanjiang, Guangdong, ChinaGuangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching, Guangdong Ocean University, Zhanjiang, Guangdong, ChinaNaval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang, ChinaAddressing the spatial variability, temporal dynamics, and non-linearity characteristics of port water levels, a hybrid prediction scheme was proposed, which integrates empirical mode decomposition (EMD) with a radial basis function neural network (RBFNN), optimized using the particle swarm optimization (PSO) algorithm. First, through the application of EMD, the port water level time series was decomposed into sub-series characterized by lower non-linearity. Subsequently, PSO was applied to fine-tune the center and spread parameters of the RBFNN, thereby enhancing the model’s predictive performance. The optimized PSO-RBFNN model was employed to make predictions on the decomposed sub-series. Finally, reconstruction of the predicted sub-series yielded the final water level predictions. The feasibility and effectiveness of the proposed model were validated using measured port water level data. Results from simulations highlighted the model’s ability to deliver accurate predictions across various lead times. Furthermore, comparative analysis revealed that the proposed model outperforms alternative methods in port water level prediction. Therefore, the proposed model serves as a reliable, efficient, and real-time prediction tool, providing robust support for port operational safety.https://www.frontiersin.org/articles/10.3389/fmars.2025.1537696/fullport water level predictionradial basis function neural networkparticle swarm optimization algorithmempirical mode decompositionhybrid model |
| spellingShingle | Lijun Wang Shenghao Liao Sisi Wang Jianchuan Yin Ronghui Li Jingyu Guan Real-time prediction of port water levels based on EMD-PSO-RBFNN Frontiers in Marine Science port water level prediction radial basis function neural network particle swarm optimization algorithm empirical mode decomposition hybrid model |
| title | Real-time prediction of port water levels based on EMD-PSO-RBFNN |
| title_full | Real-time prediction of port water levels based on EMD-PSO-RBFNN |
| title_fullStr | Real-time prediction of port water levels based on EMD-PSO-RBFNN |
| title_full_unstemmed | Real-time prediction of port water levels based on EMD-PSO-RBFNN |
| title_short | Real-time prediction of port water levels based on EMD-PSO-RBFNN |
| title_sort | real time prediction of port water levels based on emd pso rbfnn |
| topic | port water level prediction radial basis function neural network particle swarm optimization algorithm empirical mode decomposition hybrid model |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2025.1537696/full |
| work_keys_str_mv | AT lijunwang realtimepredictionofportwaterlevelsbasedonemdpsorbfnn AT shenghaoliao realtimepredictionofportwaterlevelsbasedonemdpsorbfnn AT sisiwang realtimepredictionofportwaterlevelsbasedonemdpsorbfnn AT jianchuanyin realtimepredictionofportwaterlevelsbasedonemdpsorbfnn AT ronghuili realtimepredictionofportwaterlevelsbasedonemdpsorbfnn AT jingyuguan realtimepredictionofportwaterlevelsbasedonemdpsorbfnn |