Prediction of Water Quality in Agricultural Watersheds Based on VMD-GA-LSTM Model
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/12/1951 |
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| author | Yuxuan Luo Xianglan Meng Yutong Zhai Dongqing Zhang Kaiping Ma |
| author_facet | Yuxuan Luo Xianglan Meng Yutong Zhai Dongqing Zhang Kaiping Ma |
| author_sort | Yuxuan Luo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d0f7c84cd84345f7a36a637ef779c22b |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-d0f7c84cd84345f7a36a637ef779c22b2025-08-20T03:27:40ZengMDPI AGMathematics2227-73902025-06-011312195110.3390/math13121951Prediction of Water Quality in Agricultural Watersheds Based on VMD-GA-LSTM ModelYuxuan Luo0Xianglan Meng1Yutong Zhai2Dongqing Zhang3Kaiping Ma4College of Information Management, Nanjing Agricultural University, Nanjing 211800, ChinaCollege of Information Management, Nanjing Agricultural University, Nanjing 211800, ChinaCollege of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaCollege of Information Management, Nanjing Agricultural University, Nanjing 211800, ChinaCollege of Information Management, Nanjing Agricultural University, Nanjing 211800, ChinaAs 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.https://www.mdpi.com/2227-7390/13/12/1951sustainable agriculturewater quality predictionvariational mode decompositiongenetic algorithmlong short-term memory |
| spellingShingle | Yuxuan Luo Xianglan Meng Yutong Zhai Dongqing Zhang Kaiping Ma Prediction of Water Quality in Agricultural Watersheds Based on VMD-GA-LSTM Model Mathematics sustainable agriculture water quality prediction variational mode decomposition genetic algorithm long short-term memory |
| title | Prediction of Water Quality in Agricultural Watersheds Based on VMD-GA-LSTM Model |
| title_full | Prediction of Water Quality in Agricultural Watersheds Based on VMD-GA-LSTM Model |
| title_fullStr | Prediction of Water Quality in Agricultural Watersheds Based on VMD-GA-LSTM Model |
| title_full_unstemmed | Prediction of Water Quality in Agricultural Watersheds Based on VMD-GA-LSTM Model |
| title_short | Prediction of Water Quality in Agricultural Watersheds Based on VMD-GA-LSTM Model |
| title_sort | prediction of water quality in agricultural watersheds based on vmd ga lstm model |
| topic | sustainable agriculture water quality prediction variational mode decomposition genetic algorithm long short-term memory |
| url | https://www.mdpi.com/2227-7390/13/12/1951 |
| work_keys_str_mv | AT yuxuanluo predictionofwaterqualityinagriculturalwatershedsbasedonvmdgalstmmodel AT xianglanmeng predictionofwaterqualityinagriculturalwatershedsbasedonvmdgalstmmodel AT yutongzhai predictionofwaterqualityinagriculturalwatershedsbasedonvmdgalstmmodel AT dongqingzhang predictionofwaterqualityinagriculturalwatershedsbasedonvmdgalstmmodel AT kaipingma predictionofwaterqualityinagriculturalwatershedsbasedonvmdgalstmmodel |