Akademik Veri Yönetim Sistemi
International Journal of Environmental Research, cilt.20, sa.2, 2026 (SCI-Expanded, Scopus)
This study presents a Vertical Federated Machine Learning (VFed-ML) framework designed to predict energy consumption in wastewater treatment plants (WWTPs) using actual data from a Melbourne-based facility. The effectiveness of various machine learning models including regression, ensemble learning, decision trees, and artificial neural networks were assessed for energy consumption prediction. Feature selection techniques using XGBoost and Random Forest were conducted to identify key variables that improve model performance. The proposed VFed-ML framework enables multiple agencies to collaboratively develop a unified machine learning model while ensuring the secure and efficient integration of multi-source data. The performance of the federated model was assessed over successive training rounds by analyzing the convergence of training and validation loss curves. Overall, the progressive decline in loss values during the later training rounds indicates that the model is effectively learning and improving its performance over time. The final model demonstrated improved predictive accuracy with minimal signs of overfitting, highlighting its ability to generalize well. These findings suggest that the proposed approach can be adapted to other WWTPs by incorporating plant-specific data. Key challenges such as data heterogeneity, communication bottlenecks, and model convergence, were identified and recommendations to address these issues were provided. The study also outlines opportunities for future research, including the incorporation of additional variables related to operational and external factors. In conclusion, the VFed-ML framework offers a promising solution for optimizing energy consumption predictions in WWTPs, safeguarding data privacy and security. Its flexibility allows for customization to other plants, highlighting its potential for real-world applications in environmental monitoring and resource management.