文献类型：期刊文献

英文题名：A PCA-EEMD-CNN-Attention-GRU-Encoder-Decoder Accurate Prediction Model for Key Parameters of Seawater Quality in Zhanjiang Bay

作者：Xie, Zaimi[1,2];Li, Zhenhua[2,3];Mo, Chunmei[2,3];Wang, Ji[2,3]

机构：[1]Guangdong Ocean Univ, Sch Math & Comp Sci, Zhanjiang 524088, Peoples R China;[2]Guangdong Engn Technol Res Ctr Intelligent Ocean, Zhanjiang 524088, Peoples R China;[3]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Peoples R China

年份：2022

卷号：15

期号：15

外文期刊名：MATERIALS

收录：SCI-EXPANDED(收录号：WOS:000839290300001)、、EI(收录号：20223612696140)、Scopus(收录号：2-s2.0-85137145758)、WOS

基金：This work was supported by the National Natural Science Foundation of China (51777046), the Special Project on New Generation Information Technology in Key Areas of General Universities in Guangdong Province (2020ZDZX3008), and the Special Project in Key Areas of Artificial Intelligence in Guangdong Province (2019KZDZX1046).

语种：英文

外文关键词：seawater; water quality prediction; attention; principal component analysis algorithm; ensemble empirical modal decomposition algorithm; sea area near Zhanjiang Bay

外文摘要：In order to effectively solve the problem of low accuracy of seawater water quality prediction, an optimized water quality parameter prediction model is constructed in this paper. The model first screened the key factors of water quality data with the principal component analysis (PCA) algorithm, then realized the de-noising of the key factors of water quality data with an ensemble empirical mode decomposition (EEMD) algorithm, and the data were input into the two-dimensional convolutional neural network (2D-CNN) module to extract features, which were used for training and learning by attention, gated recurrent unit, and an encoder-decoder (attention-GRU-encoder-decoder, attention-GED) integrated module. The trained prediction model was used to predict the content of key parameters of water quality. In this paper, the water quality data of six typical online monitoring stations from 2017 to 2021 were used to verify the proposed model. The experimental results show that, based on short-term series prediction, the root mean square error (RMSE), mean absolute percentage error (MAPE), and decision coefficient (R-2) were 0.246, 0.307, and 97.80%, respectively. Based on the long-term series prediction, RMSE, MAPE, and R-2 were 0.878, 0.594, and 92.23%, respectively, which were all better than the prediction model based on an enhanced clustering algorithm and adam with a radial basis function neural network (ECA-Adam-RBFNN), a prediction model based on a softplus extreme learning machine method with partial least squares and particle swarm optimization (PSO-SELM-PLS), and a wavelet transform-depth Bi-S-SRU (Bi-directional Stacked Simple Recurrent Unit) prediction model. The PCA-EEMD-CNN-attention-GED prediction model not only has high prediction accuracy but can also provide a decision-making basis for the water quality control and management of aquaculture in the waters around Zhanjiang Bay.