Ensemble water quality forecasting based on decomposition, sub-model selection, and adaptive interval.

Area of Science:

• Environmental Engineering
• Water Treatment Technology
• Predictive Modeling

Background:

• Accurate effluent quality prediction is crucial for wastewater treatment plants (WWTPs) to meet water standards and optimize energy use.
• The inherent nonlinearity of WWTPs complicates forecasting and often overlooks prediction uncertainty.
• Existing methods struggle to simultaneously address both point prediction accuracy and forecast uncertainty.

Purpose of the Study:

• To develop a novel ensemble water quality forecasting (EWQF) system for WWTPs.
• To accurately predict effluent quality while also quantifying forecast uncertainty.
• To enable effective feed-forward control strategies for WWTPs.

Main Methods:

• Improved Variational Modal Decomposition (IVMD) for data preprocessing and noise reduction.
• Ensemble modeling combining optimal sub-models for point prediction.
• Adaptive kernel density estimation for robust interval prediction and uncertainty analysis.

Main Results:

• The EWQF system achieved excellent point prediction accuracy, with R² = 0.955.
• The system demonstrated optimal performance based on the Coverage Width Criterion (CWC) for interval prediction across various confidence levels.
• EWQF effectively analyzed effluent quality prediction uncertainty.

Conclusions:

• The proposed EWQF system provides accurate effluent quality point predictions.
• EWQF successfully quantifies prediction uncertainty, offering reliable intervals.
• This system offers a robust solution for enhancing WWTP operational control and performance.