基于EEMD-LSTM的丹江口水库水质智能预测模型研究OA

To address the limitations of traditional water quality prediction models in prediction accuracy and timeliness,this study develops an intelligent water quality prediction model for the Danjiangkou Reservoir based on the EEMD-LSTM algorithm.The model enhances data quality and optimizes feature selection through a three-stage progressive preprocessing pipeline and du-al-dimensional feature analysis.Subsequently,it utilizes Ensemble Empirical Mode Decomposition(EEMD)to mitigate mode mixing and extract multi-scale temporal features,and leverages the advantages of Long Short-Term Memory(LSTM)networks in capturing long-term temporal dependencies to achieve high-precision prediction.Experimental results demonstrate that,for total phosphorus concentration prediction at five monitoring stations,the model achieves an R2 value above 0.8 for the 1～3 day short-term prediction horizon and an R2 above 0.65 for the 7-day medium-term prediction horizon.The results indicate that the EEMD-LSTM model effectively overcomes the inherent prediction lag of standalone LSTM models and significantly improves the accuracy and robustness of water quality forecasting.This study can provide technical support for real-time water quality fore-casting and risk management of the Danjiangkou Reservoir.