Prediction of Water Quality in Agricultural Watersheds Based on VMD-GA-LSTM Model

Yuxuan Luo, Xianglan Meng, Yutong Zhai, Dongqing Zhang () and Kaiping Ma
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Yuxuan Luo: College of Information Management, Nanjing Agricultural University, Nanjing 211800, China
Xianglan Meng: College of Information Management, Nanjing Agricultural University, Nanjing 211800, China
Yutong Zhai: College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Dongqing Zhang: College of Information Management, Nanjing Agricultural University, Nanjing 211800, China
Kaiping Ma: College of Information Management, Nanjing Agricultural University, Nanjing 211800, China

Mathematics, 2025, vol. 13, issue 12, 1-19

Abstract: As agricultural non-point source pollution becomes increasingly severe and constitutes the primary source of water quality degradation, accurately predicting water quality in agricultural watersheds has become critical for environmental protection. In order to solve the nonlinear and non-stationary characteristics of water quality data, this paper proposes a combined model based on variational modal decomposition and genetic algorithm optimization of long short-term memory networks (VMD-GA-LSTM) for agricultural watershed water quality prediction. The VMD-GA-LSTM model utilizes the variational mode decomposition technique to decompose the time series data into multiple intrinsic mode functions and then uses the optimized LSTM network to predict each component to improve the accuracy of water quality prediction. The analysis of water quality data from the Baima River in China demonstrated that the VMD-GA-LSTM model significantly reduced prediction errors compared to other similar models. The VMD-GA-LSTM predictive model proposed in this paper effectively addresses the volatility characterizing water quality in agricultural watersheds, improves prediction accuracy, and it reveals valuable trends in water quality dynamics, providing practical solutions for sustainable agricultural practices and environmental governance.

Keywords: sustainable agriculture; water quality prediction; variational mode decomposition; genetic algorithm; long short-term memory (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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