## 3 Performance Analysis of the Data-Driven Models
The MLPNN model trained using the Bayesian Regularization algorithm was capable of learning the nonlinear relationship between the irradiation time, initial phenol concentration, pH, photocatalyst dosage, and phenol degradation. The predictability of the MLPNN was evidenced by the RMSE of 1.126 and R\({}^{2}\) of 0.999. The performance analysis of the EQGPR model as a comparison between the actual and the predicted BPA degradation, actual and predicted BPA degradation rate, and actual and the predicted rate of initial BPA concentration is depicted in Figure 5. Figure 5a shows that the actual values of BPA degradation slightly agree with the predicted values using EQGPR at each data point. As indicated by R\({}^{2}\) of 0.763 and RMSE of 15.819, the EQGPR is not robust enough to generalize the interrelationship between the textural properties and the BPA degradation efficiency. Similarly, in Figure 5b,c, there is a wide disparity between the actual and the predicted rate of BPA degradation from industrial wastewater. This is an indication that the EQGPR model is not robust enough to model the interconnectivity between the textural properties of the catalyst, the BPA degradation, and the initial concentration of the BPA. Although the EQGPR model did not display an impressive performance in modeling the prediction of the BPA degradation, the rate of BPA degradation, and the initial BPA concentration, studies have shown that the EQGPR is effective in modeling other processes. [PERSON] et al. [57] demonstrated the application of the GPR model in the prediction of building electricity usage. After comparing the observed real data to the predicted results, the suggested algorithms' dependability and efficiency were shown to be conclusive. Data prediction accuracy with average deviations below 15% and minimal computation time are shown to be the sweet spot for which GPR may provide reasonable prediction on the energy usage of office buildings. The performance of the GPR could be improved by using different kernel functions. The kernel function is a technique for taking raw data and transforming it into the desired shape for processing. Figure 6 depicts the performance of the CSVMR model in the form of a comparison between the actual and the predicted BPA degradation, the actual and predicted BPA degradation rate, and the actual and the predicted rate of initial BPA concentration. It can be seen that there is a wide disparity between the actual and the predicted values as indicated in Figure 6a-c. This can further be confirmed by the R\({}^{2}\) of 0.397 and the RMSE values of 19.591, which is an indication that the CSVMR model is not robust enough to generalize the modeling of the relationship between the textural properties and the BPA degradation efficiency, the BPA degradation rate, and the initial BPA concentration. Nevertheless, the SVMR models have been successfully applied in modeling other processes. [PERSON] et al. [58] demonstrated the robustness of the SVMR in modeling the heat exchanger performance  
in cryogenic oscillating flow conditions. With an R\({}^{2}\) of 0.922 and a maximum error of 12.4%, the SVMR model displayed an accurate prediction. Heat transfer characteristics in cryogenic oscillating flow may be processed using the SVMR model, and the findings show that it is both an economical and precise approach. As a recommendation for further studies, the improvement of the SVMR used in this study can further be investigated using different types of kernel functions. Figure 4: (**a**) Comparison between the actual and the MLPNN predicted BPA degradation; (**b**) comparison between the actual and the MLPNN predicted BPA degradation rate; (**c**) comparison between the actual and the MLPNN predicted rate of initial BPA concentration. Figure 5: (**a**) Comparison between the actual and the EQGPR predicted BPA degradation; (**b**) comparison between the actual and the EQGPR predicted BPA degradation rate; (**c**) comparison between the actual and the EQGPR predicted rate of initial BPA concentration.