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This article presents a lifelong learning framework for predicting viral particles in wastewater treatment plants (WWTPs) using machine learning models like LSTM and GRU, combined with synthetic data generation techniques. The proposed approach aims to enhance predictive accuracy and adaptability across various WWTPs by leveraging real-time process inputs and knowledge transfer mechanisms. The study demonstrates the effectiveness of this framework in accurately predicting viral concentrations, addressing ch

Author

patrickmleung

Language

EN