ML-quantitative-structure-property-relationship (ML-QSPR) methods can calculate the rate constant (kO3) of the reactions between ozone and micropollutants . Generally, nonlinear models outperform their linear counterparts. For instance, an MLR method (Sudhakaran and Amy, 2013) and an SVM method have R 2 values of greater than 0.75 and 0.78, respectively. Conversely, a DTB model has a higher R 2 value of greater than 0.97 . A recent study compared several ML models including MLR, SVM, DT, RF, and deep neural network (DNN) for predicting logkO3 . Of these, RF has the highest effectiveness with a peak R 2 value of 0.91. RF has two primary benefits: robustness and a lower tendency of overfitting. On the other hand, DT has a complex structure and subsequently increases the overfitting risk. In addition, DNN, promising at recognizing nonlinear features, underperforms in predicting logkO3. A similar situation occurs when ML models predict the elimination of recalcitrant trace organic compounds (TOrCs) by ozonation for municipal wastewater reuse . Specifically, ANN is susceptible to overfitting. Incorporating PCA into ANN creates a PC-ANN workflow, which addresses the overfitting issue. PCA transforms the input variables (Table ) to linearly independent variables, thereby resolving the issue of collinearity among explanatory variables.