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

Abstract

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.

Keywords: Data mining; Gated recurrent unit; Pearson correlation analysis; Variational modal decomposition; Water quality prediction.