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Application of a wavelet denoising-based LSTM-Transformer model for water quality prediction at river cross-sections
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Ketong XU1, Hongbin ZENG1, Liping CHEN1, Qianyun ZHENG1, Hang CHEN1, Xiaojing XIE1, Jing YUAN1, Chaohai WEI1, 2, 3, Guanglei QIU1, 2, 3, 4
Environmental Engineering | 2026, 44(3) : 125 - 135
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Environmental Engineering | 2026, 44(3): 125-135
Application of a wavelet denoising-based LSTM-Transformer model for water quality prediction at river cross-sections
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Ketong XU1, Hongbin ZENG1, Liping CHEN1, Qianyun ZHENG1, Hang CHEN1, Xiaojing XIE1, Jing YUAN1, Chaohai WEI1, 2, 3, Guanglei QIU1, 2, 3, 4
Affiliations
  • 1School of Environment and Energy,South China University of Technology,Guangzhou 510006,China
  • 2Key Laboratory of Pollution Control and Ecological Restoration in Industrial Clusters,Ministry of Education,Guangzhou 510006,China
  • 3Guangdong Provincial Key Laboratory of Solid Wastes Pollution Control and Recycling,Guangzhou 510006,China
  • 4National Joint Research Center for Ecological Conservation and High Quality Development of the Yellow River Basin,Beijing 100012,China
Published: 2026-03-22 doi: 10.13205/j.hjgc.202603011
Outline
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This study proposed a hybrid Long Short-Term Memory (LSTM)-Transformer model integrated with wavelet denoising for water quality prediction. Using hourly monitoring data (water temperature, turbidity, pH, conductivity, and dissolved oxygen) collected from two municipally controlled river cross-sections in South China from 2021 to 2024, the discrete wavelet transform was first applied for noise reduction. Subsequently, a predictive model combining LSTM and Transformer architectures was constructed. Experimental results demonstrated that the proposed model achieved outstanding performance in predicting dissolved oxygen (DO) concentrations for the next four hours at both sites (Site 1: coefficient of determination (R²)=0.8015, mean absolute error (MAE)=0.5169 mg/L, root mean square error (RMSE)=0.8494 mg/L; Site 2: R²=0.8873, MAE=0.4456 mg/L, RMSE=0.7143 mg/L), significantly outperforming standalone LSTM and Transformer models (the R² of the proposed model increased by 5.7%, while MAE and RMSE decreased by 20.2% and 10.4%, respectively).Furthermore, the SHAP interpretability method was employed for feature importance analysis and global impact interpretation, revealing that the key water quality factors influencing DO and their complex nonlinear relationships exhibited significant site-specific heterogeneity. This underscores the necessity of incorporating specific environmental contexts (e.g., geographical features, hydrological conditions, and pollution source distribution) for mechanistic interpretation. The findings of this study provide an effective and interpretable technical reference for high-precision real-time prediction and intelligent management of regional river water quality.

water quality prediction  /  deep learning  /  wavelet denoising  /  LSTM-Transformer model  /  SHAP interpretability analysis
Ketong XU, Hongbin ZENG, Liping CHEN, Qianyun ZHENG, Hang CHEN, Xiaojing XIE, Jing YUAN, Chaohai WEI, Guanglei QIU. Application of a wavelet denoising-based LSTM-Transformer model for water quality prediction at river cross-sections[J]. Environmental Engineering, 2026 , 44 (3) : 125 -135 . DOI: 10.13205/j.hjgc.202603011
Year 2026 volume 44 Issue 3
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Article Info
doi: 10.13205/j.hjgc.202603011
  • Receive Date:2026-02-01
  • Online Date:2026-06-25
  • Published:2026-03-22
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History
  • Received:2026-02-01
  • Revised:2026-03-09
  • Accepted:2026-03-10
Affiliations
    1School of Environment and Energy,South China University of Technology,Guangzhou 510006,China
    2Key Laboratory of Pollution Control and Ecological Restoration in Industrial Clusters,Ministry of Education,Guangzhou 510006,China
    3Guangdong Provincial Key Laboratory of Solid Wastes Pollution Control and Recycling,Guangzhou 510006,China
    4National Joint Research Center for Ecological Conservation and High Quality Development of the Yellow River Basin,Beijing 100012,China
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表12种不同金属材料的力学参数

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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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