AI-driven wastewater management through comparative analysis of feature selection techniques and predictive models

Area of Science:

• Environmental Engineering
• Data Science
• Water Resource Management

Background:

• Wastewater treatment management requires accurate effluent quality prediction for regulatory compliance and operational efficiency.
• Artificial intelligence (AI) presents a novel approach to enhance these predictions and optimize treatment processes.

Purpose of the Study:

• To evaluate the performance of various machine learning models in predicting key wastewater effluent parameters.
• To identify the most significant predictors for effluent quality using feature selection techniques.
• To compare the efficacy of ensemble learning models against Decision Tree models.

Main Methods:

• Applied feature selection techniques: SelectKBest, Mutual Information, and Recursive Feature Elimination (RFE).
• Utilized ensemble models (XGBoost, Random Forest, Gradient Boosting, LightGBM) and Decision Trees.
• Predicted Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), Total Suspended Solids (TSS), Total Effluent Nitrogen, and Total Effluent Phosphorus.

Main Results:

• Effluent volatile suspended solids (VSS) consistently emerged as the most important predictor.
• Ensemble models significantly outperformed Decision Trees in predictive accuracy.
• Gradient Boosting excelled in predicting TSS and total nitrogen; XGBoost for COD and BOD; LightGBM for total phosphorus.

Conclusions:

• AI-driven approaches, especially ensemble methods, offer substantial improvements in wastewater effluent quality prediction.
• Accurate prediction enhances decision-making, regulatory compliance, and resource efficiency in wastewater management.
• Operational irregularities and seasonal variations present ongoing challenges for AI model refinement.