Knowledge-Based Feature Selection Substantially Enhances Data-Driven Wastewater Treatment Modeling.

Area of Science:

• Environmental Engineering
• Wastewater Treatment
• Data Science

Background:

• Data-driven modeling in wastewater treatment faces challenges with small, high-dimensional datasets.
• Abundant monitoring parameters can obscure fundamental mechanistic understanding.
• Accurate prediction of nitrous oxide (N2O) emissions is crucial for environmental management.

Purpose of the Study:

• To propose a knowledge-driven feature selection framework integrating mechanistic insights and statistical correlations.
• To identify the most informative predictive features for wastewater treatment processes.
• To enhance the accuracy and generalizability of predictive models for N2O emissions.

Main Methods:

• A case study on N2O emission prediction at a full-scale wastewater treatment plant.
• Comparison of deep-learning attention mechanisms with expert-guided and LLM-augmented feature selection.
• Evaluation of model performance using R-squared and Mean Absolute Error (MAE) metrics.

Main Results:

• Expert-guided feature selection achieved a mean R-squared of 0.723 and MAE of 0.033, outperforming attention-based models.
• Knowledge-driven approaches significantly improved model generalizability, particularly under out-of-distribution conditions.
• LLM-augmented feature selection demonstrated competitive accuracy (R-squared = 0.596, MAE = 0.041) and preserved generalizability.

Conclusions:

• Integrating mechanistic understanding into feature selection is a powerful strategy for wastewater treatment modeling.
• Knowledge-driven frameworks offer a generalizable pathway to address complex challenges in environmental data analysis.
• This approach enhances predictive accuracy and model robustness while maintaining computational efficiency.