Against the backdrop of China's implementation of the"dual carbon"strategy,pre-dicting N2O emissions from wastewater treatment plants(WWTPs)has significance for carbon neutrality in WWTPs.The existing studies on N2O emission prediction is usually directly based on modeling and prediction of N2O emission data containing noise,resulting in low prediction accuracy of the model.In this study,a model combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Long Short-Term Memory(LSTM),referred to as the CEEMDAN-LSTM model,is introduced.The CEEMDAN methodology is introduced to alleviate the impact of noise in the data,enhancing the model's predictive accuracy.The model is applied to forecast N2O emissions from WWTPs and is validated on a test dataset.Compared to models such as LSTM,Gated Recurrent Unit(GRU),Artificial Neural Networks(ANN)and Support Vector Machine(SVM),the CEEMDAN-LSTM model demonstrates superior performance in terms of Mean Squared Error(MSE),Mean Absolute Error(MAE),and Mean Absolute Percentage Error(MAPE),with values of 5 497.11,56.55,and 1.22％,respectively.It can more accurately pre-dict N2O emissions,providing theoretical support for wastewater treatment plants to adopt appro-priate carbon offset strategies.
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