Improved prediction of chlorophyll-a concentrations in reservoirs by GRU neural network based on particle swarm algorithm optimized variational modal decomposition

• Published in: Environmental research Vol. 221; p. 115259
• Main Authors: Zhang, Xihai, Chen, Xianghui, Zheng, Guochen, Cao, Guangli
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 15.03.2023
• Subjects: Algorithms; Chlorophyll; Chlorophyll A; Data mining; eutrophication; Gated recurrent unit; Neural Networks, Computer; Pearson correlation analysis; pollution control; prediction; sample size; Variational modal decomposition; Water Quality; Water quality prediction; Variational modal decomposition; Gated recurrent unit; Pearson correlation analysis; Water quality prediction; Data mining
• ISSN: 0013-9351; 1096-0953; 1096-0953
• DOI: 10.1016/j.envres.2023.115259

The accurate and reliable prediction of chlorophyll-a (Chl-a) concentration is of great significance in reservoir environment management and pollution control. To improve the accuracy of Chl-a index prediction, a novel hybrid water quality prediction method was proposed for gated recurrent unit (GRU) neural network based on particle swarm algorithm optimized variational modal decomposition (PV-GRU). The results showed that the variational mode decomposition (VMD) optimized by particle swarm optimization (PSO) in this study effectively reduced the non-smooth of water quality data. In addition, the GRU neural network reduced the risk of overfitting the deep-learning model with small sample data. Overall, the PV-GRU prediction model exhibited significant superiority in predicting non-smooth and non-linear Chl-a sequences with a relatively small sample size. The prediction errors of PV-GRU model were all less than those of other comparative models, and the fitting determination coefficient R2 was 94.21%. These results indicated that the proposed PV-GRU model can effectively predict the content of Chl-a in reservoirs, which provides an alternative new method for water quality prediction to prevent and control eutrophication in reservoirs. [Display omitted] •A novel hybrid model of PV-GRU was proposed to predict Chl-a concentration.•The VMD optimized by PSO reduced the non-stationary of water quality data.•Influencing factors affecting Chl-a was explored by Pearson correlation analysis.•GRU neural network was constructed to fit small sample water quality data.•The prediction accuracy R2 of PV-GRU reached a high level of 94.21%.