A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM

Highlights

● A novel VMD-IGOA-LSTM model has proposed for the prediction of water quality.

● Improved model quickly converges to the global optimal fitness and remains stable.

● The prediction accuracy of water quality parameters is significantly improved.

Abstract

Water quality prediction is vital for solving water pollution and protecting the water environment. In terms of the characteristics of nonlinearity, instability, and randomness of water quality parameters, a short-term water quality prediction model was proposed based on variational mode decomposition (VMD) and improved grasshopper optimization algorithm (IGOA), so as to optimize long short-term memory neural network (LSTM). First, VMD was adopted to decompose the water quality data into a series of relatively stable components, with the aim to reduce the instability of the original data and increase the predictability, then each component was input into the IGOA-LSTM model for prediction. Finally, each component was added to obtain the predicted values. In this study, the monitoring data from Dayangzhou Station and Shengmi Station of the Ganjiang River was used for training and prediction. The experimental results showed that the prediction accuracy of the VMD-IGOA-LSTM model proposed was higher than that of the integrated model of Ensemble Empirical Mode Decomposition (EEMD), the integrated model of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Nonlinear Autoregressive Network with Exogenous Inputs (NARX), Recurrent Neural Network (RNN), as well as other models, showing better performance in short-term prediction. The current study will provide a reliable solution for water quality prediction studies in other areas.

Graphical abstract

Keywords

Water quality prediction / Grasshopper optimization algorithm / Variational mode decomposition / Long short-term memory neural network

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Acknowledgements

This work was supported by the Zhejiang Provincial Natural Science Foundation of China (No. LY23H180001), the Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, the China Institute of Water Resources and Hydropower Research (No. IWHR-SKL-201905) and the National Natural Science Foundation of China (No. 11701363).

Data Accessibility Statement

The data and code that support the findings of this study are available from the corresponding author, Tunhua Wu, upon reasonable request.

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