文献类型：期刊文献

英文题名：Real-time prediction of port water levels based on EMD-PSO-RBFNN

作者：Wang, Lijun[1];Liao, Shenghao[1];Wang, Sisi[1];Yin, Jianchuan[2];Li, Ronghui[2];Guan, Jingyu[1]

机构：[1]Guangdong Ocean Univ, Naval Architecture & Shipping Coll, Zhanjiang, Peoples R China;[2]Guangdong Ocean Univ, Guangdong Prov Key Lab Intelligent Equipment South, Zhanjiang, Guangdong, Peoples R China

年份：2025

卷号：12

外文期刊名：FRONTIERS IN MARINE SCIENCE

收录：SCI-EXPANDED(收录号：WOS:001413625100001)、、Scopus(收录号：2-s2.0-85216951086)、WOS

基金：The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was funded by the National Science Foundation of China, grant number 52171346 and grant number 52271361; the Fund of Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching, grant number 2023B1212030003; and Key Area Project of Ordinary Universities in Guangdong Province, grant number 2024ZDZX3054.

语种：英文

外文关键词：port water level prediction; radial basis function neural network; particle swarm optimization algorithm; empirical mode decomposition; hybrid model

外文摘要：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.