A PCA–EEMD–CNN–Attention–GRU–Encoder–Decoder Accurate Prediction Model for Key Parameters of Seawater Quality in Zhanjiang Bay

Abstract

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 (R2) were 0.246, 0.307, and 97.80%, respectively. Based on the long-term series prediction, RMSE, MAPE, and R2 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.

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“…PCA transforms highdimensional data into a lower-dimensional space while preserving the most important patterns and variations present in the original data. It achieves this by identifying the principal components, which are new orthogonal axes that represent the directions of maximum variance in the data [24]. The "Synthetic Minority Over-sampling Technique" (SMOTE) is a popular data augmentation method used to address class imbalance in machine learning.…”

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“…These impacts pose serious challenges to the ecological environment and the aquaculture industry. Therefore, Zaimi Xie et al were committed to optimizing the algorithm and proposed the PCA-EEMD-CNN-Attention-GED seawater aquaculture water quality prediction model, which changed the traditional on-site water quality monitoring method [9]. The root means square error (RMSE), mean absolute percentage error (MAPE), and decision coefficient (R2) of the obtained short-term monitoring data were 0.246%, 0.307%, and 97.80%, respectively, and the predicted RMSE, MAPE, and R2 of their long-term series were 0.878%, 0.594%, and 92.23%, respectively.…”