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The accurate prediction of wastewater quality parameters is pivotal for evaluating the treatment stability of processes and for ensuring regulatory compliance in wastewater treatment plants. A singular machine learning model often faces challenges in fully capturing and extracting the complex nonlinear relationships inherent in multivariate time series data. To overcome this limitation, this study proposes a dual hybrid modeling framework that effectively integrates LSTM and XGBoost models, leveraging their complementary strengths. The first hybrid model refines the residues to utilize the information, whereas the second hybrid model enhances the input features by extracting temporal dependencies. A comparative analysis against three standalone models reveals that the proposed hybrid framework consistently outperforms them in both predictive accuracy and generalization ability across four key effluent indicators—chemical oxygen demand, ammonia nitrogen, total nitrogen, and total phosphorus. These results demonstrate that the proposed hybrid machine learning framework has great potential to be used to evaluate process stability in wastewater treatment plants, paving a way for smarter, more resilient, and more sustainable wastewater management, which will improve ecological integrity and regulatory compliance.
Xiong et al. (Sat,) studied this question.