## 2 Data Acquisition and Model Configuration  
The dataset used for testing the model configuration consist of various combinations of parameters representing the textural properties of the photocatalysts. These parameters include the pore diameter, the BET-specific surface area, the pore volume, and the crystallite size. The various combinations of the dataset were obtained using a central composite experimental design as detailed by [PERSON] et al. [39]. Based on the CCD formulation, the Er and Fe amounts used for doping the TiO\({}_{2}\) photocatalysts were varied together with the calcination temperature for the photocatalysts. The textural properties such as the BET-specific surface area, the photocatalyst particle size, and the pore volumes for each of the formulations were measured as detailed by [PERSON] et al. [39]. The crystallite size for each of the combinations was obtained from the XRD analysis. The details of the textural data obtained for each of the photocatalyst formulations are reported by [PERSON] et al. [40]. A typical experimental set-up shown in Figure 1 is often employed for contaminant photocatalytic degradation [40]. As reported by [PERSON] et al. [39], the photocatalyst activity of Fe/Er-TiO\({}_{2}\) in degrading the BPA from industrial wastewater was investigated in a photoreactor. Before the authors initiated the photocatalytic experiment, a 200 mL solution containing 200 mg photocatalysts and 10 mg/L of BPA was ultrasonically mixed for 5 min and magnetically stirred for 60 min. The photocatalytic degradation reaction was initiated by turning on the light source. After the photodegradation reaction, the solution was filtered using cellulose acetate syringe membrane filters and analyzed with an HPLC. Photocatalytic degradation of BPA was also examined in terms of photocatalyst dosage, light sources, initial BPA concentration, and aqueous matrix anions. The configuration employed for this study consists of a multilayer perceptron neural network with a back-propagation algorithm (MLPNN), cubic support vector machine regression (SVMR), and exponential quadratic Gaussian process regression (EQGRP). The MLPNN, SVMR, and EQGPR were employed to model the relationship between the textural  
Figure 1: Setup illustrating the photocatalytic degradation of the Bisphenol from industrial wastewater effluent. properties and the performance of modified TiO\({}_{2}\) in degrading the BPA [41]. The MLPNN consists of three layers, namely, input, output, and hidden. The input layer receives the processed signal. The output layer performs tasks like prediction and categorization. One or more hidden layers between the input and output layers make up the MLP's true computational engine. An MLP is a feed-forward network with data flowing from the input to the output layer. The MLP's neurons are taught using back-propagation learning. For problems that cannot be solved linearly, MLPs can approximate continuous functions. In this study, several configurations of the MLPNN were tested to determine the architecture with the best performance. The support vector machine approach for binary response variables served as inspiration for the development of support vector regression [42]. The algorithm's fundamental notion is to only make use of residuals whose absolute values are less than a certain threshold. Finding a well-fitting hyperplane in a kernel-induced feature space with acceptable generalization performance utilizing the original features is the basic idea behind the SVMR [43]. The CSVMR model was trained with the help of _fitrsvm_ in the MATLAB environment to achieve higher levels of accuracy while working with low- to medium-dimensional datasets. In a GPR model, the anticipated value of the target variable is specified by the covariance function, which describes how the expected value varies when values are transformed throughout the input space [44]. In this case, a quadratic exponential covariance function with automated relevance determination was used.