Application of an improved LSTM model based on FECA and CEEMDAN VMD decomposition in water quality prediction.

To address the limitations of existing water quality prediction models in handling non-stationary data and capturing multi-scale features, this study proposes a hybrid model integrating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variational Mode Decomposition (VMD), Long Short-Term Memory Network (LSTM), and Frequency-Enhanced Channel Attention (FECA). The model aims to improve prediction accuracy and robustness for complex water quality dynamics, which is critical for environmental protection and sustainable water resource management. First, CEEMDAN and Sample Entropy (SE) were used to decompose raw water quality data into interpretable components and filter noise. Then, a VMD-enhanced LSTM architecture embedded with FECA was developed to adaptively prioritize frequency-specific features, thereby improving the model's ability to handle nonlinear patterns. Results show that the model is successful in predicting all six water quality indicators: NH₃-N (ammonia nitrogen), DO (dissolved oxygen), pH, TN (total nitrogen), TP (total phosphorus), and CODMn (chemical oxygen demand, permanganate method). The model achieved Nash-Sutcliffe Efficiency (NSE) values ranging from 0.88 to 0.99. Using dissolved oxygen (DO) as an example, the model reduced the Mean Absolute Percentage Error (MAPE) by 0.12% and increased the coefficient of determination (R2) by 0.20% compared to baseline methods. This work provides a robust framework for real-time water quality monitoring and supports decision making in pollution control and ecosystem management.