Variable Selection and Model Comparison for Optimizing Machine Learning-Based TOC Prediction

This study developed a rapid and real-time model for predicting total organic carbon (TOC), which is an alternative to the conventional biochemical oxygen demand (BOD) and chemical oxygen demand (COD) indicators. The influence of input variable selection methods and machine learning hyperparameter tuning on TOC prediction accuracy was compared using ten-year water quality monitoring data. The analysis showed that TOC exhibited strong correlations with COD, T-P, BOD, and ammonia nitrogen (NH3-N). Principal component analysis confirmed that the primary factors driving TOC variation were associated with organic matter and nutrient pollution. Prediction models were developed using a multilayer perceptron (MLP) and random forest (RF). On average, the MLP model outperformed the RF model by approximately 20%, and COD consistently appeared as a critical predictor in all top-ranked feature sets. Finally, grid search-based hyperparameter tuning of the MLP model with the optimal variable set (DO, COD, T-P, DTP, PO4-P) increased the coefficient of determination from 0.7496 to 0.7562. The findings demonstrate that precise exploration of variable combinations and stronger model regularization are essential for improving prediction performance in TOC modeling. This study provides a foundation for future development of predictive models that integrate external environmental factors such as nonpoint source pollution.