Application of signal decomposition and deep learning integration strategies in water quality prediction

ZHENG Zhiyan; GUAN Xianghong; QIU Guanglei; WU Haizhen; WEI Chaohai

doi:10.13205/j.hjgc.202610008

Volume 44 Issue 10

Oct. 2026

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2026 > 44(10): 74-85

ZHENG Zhiyan, GUAN Xianghong, QIU Guanglei, WU Haizhen, WEI Chaohai. Application of signal decomposition and deep learning integration strategies in water quality prediction[J]. ENVIRONMENTAL ENGINEERING , 2026, 44(10): 74-85. doi: 10.13205/j.hjgc.202610008

Citation:

ZHENG Zhiyan, GUAN Xianghong, QIU Guanglei, WU Haizhen, WEI Chaohai. Application of signal decomposition and deep learning integration strategies in water quality prediction[J]. ENVIRONMENTAL ENGINEERING , 2026, 44(10): 74-85. doi: 10.13205/j.hjgc.202610008

Citation:

ZHENG Zhiyan, GUAN Xianghong, QIU Guanglei, WU Haizhen, WEI Chaohai. Application of signal decomposition and deep learning integration strategies in water quality prediction[J]. ENVIRONMENTAL ENGINEERING , 2026, 44(10): 74-85. doi: 10.13205/j.hjgc.202610008

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Application of signal decomposition and deep learning integration strategies in water quality prediction

doi: 10.13205/j.hjgc.202610008

1. School of Environment and Energy, South China University of Technology, Guangzhou 510006, China;
2. School of Biomedical and Engineering, South China University of Technology, Guangzhou 510006, China

Received Date: 2026-03-26
Available Online: 2026-11-10

Abstract

Abstract

Accurate water quality prediction serves as a key step in facilitating the transformation of water environment management models from post-event response to proactive prevention. To address the nonlinear， non-stationary， and multi-scale characteristics of water quality time series and the challenges of accurate prediction， this study proposed a novel water quality prediction strategy that integrates signal decomposition with deep learning models. Based on water quality monitoring data collected from multiple state-controlled monitoring sections from January 2021 to December 2024， a CEEMDAN-TCN-LSTM water quality prediction model via Bayesian optimization was eatablished relying solely on routine water quality parameters. The model demonstrated excellent robustness and generalization performance across three dimensions， with average coefficients of determination （R²） of 0.93 for multi-indicator predictions， 0.96 for multi-site predictions， and 0.96 for multi-step predictions. The employed complete ensemble empirical mode decomposition with adaptive noise （CEEMDAN） effectively extracted multi-scale information from the water quality sequences， reducing the prediction error of the hybrid neural network （TCN-LSTM） model by 68%. To address the surge in input variables caused by signal decomposition， this study introduced Sample Entropy （SampEn） alongside adaptive recombination rules； this approach successfully reduced the computational load by over 50% while keeping accuracy loss within a strictly defined acceptable range. Furthermore， Bayesian optimization （BO） improved the prediction accuracy and operational flexibility of the model. By breaking the black-box limitation of data-driven models， Shapley additive explanations （SHAP） analysis revealed significant differences in the importance ranking of feature variables across different components of the dissolved oxygen （DO） time series， and the contribution pattern of individual indicators also varied with scale， underscoring the necessity of incorporating multi-scale information to enhance the responsiveness of water quality prediction models to complex fluctuations. Grounded in the multi-scale characteristics of water quality time series， this study enriches the methodology of water quality prediction modeling， makes full use of existing water quality data resources from river section monitoring systems， and provides technical support for advancing intelligent water environment management.
- water quality prediction,
- signal decomposition,
- LSTM-TCN model,
- Bayesian optimization,
- SHAP interpretability analysis

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References(30)

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