An Empirical Modal Decomposition-Improved Whale Optimization Algorithm-Long Short-Term Memory Hybrid Model for Monitoring and Predicting Water Quality Parameters

Binglin Li (), Hao Xu, Yufeng Lian, Pai Li, Yong Shao and Chunyu Tan
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Binglin Li: School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China
Hao Xu: School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China
Yufeng Lian: School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China
Pai Li: School of Computer and Automation, Wuhan Technology and Business University, Wuhan 430070, China
Yong Shao: School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China
Chunyu Tan: School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China

Sustainability, 2023, vol. 15, issue 24, 1-18

Abstract: Prediction of water quality parameters is a significant aspect of contemporary green development and ecological restoration. However, the conventional water quality prediction models have limited accuracy and poor generalization capability. This study aims to develop a dependable prediction model for ammonia nitrogen concentration in water quality parameters. Based on the characteristics of the long-term dependence of water quality parameters, the unique memory ability of the Long Short-Term Memory (LSTM) neural network was utilized to predict water quality parameters. To improve the accuracy of the LSTM prediction model, the ammonia nitrogen data were decomposed using Empirical Modal Decomposition (EMD), and then the parameters of the LSTM model were optimized using the Improved Whale Optimization Algorithm (IWOA), and a combined prediction model based on EMD-IWOA-LSTM was proposed. The study outcomes demonstrate that EMD-IWOA-LSTM displays improved prediction accuracy with reduced RootMean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) in comparison to the LSTM and IWOA-LSTM approaches. These research findings better enable the monitoring and prediction of water quality parameters, offering a novel approach to preventing water pollution rather than merely treating it afterwards.

Keywords: water quality parameter prediction; empirical modal decomposition; whale optimization algorithm; long and short-term memory neural networks (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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