## 3 Results and Discussion
### SVR-GO-BFS\({}_{n}\) Model Performance  
Following recognizing the importance level of the inputs by BFS, an SVR model kernelized with RBF is employed to carry out the predictive analysis. During running the model, \(\gamma\) and C that are pair hyperparameters of SVR are optimized by the GO algorithm. Nine SVR-GO-BFS\({}_{n}\) models (SVR-GO-BFS\({}_{1}\) to SVR-GO-BFS\({}_{9}\)) are developed based on nine various inputs sets (n = 1 set to n = 9). The n = 1 set comprises just the first most significant input, while the n = 9 collection encompasses all nine vital inputs estimated by BFS. To choose among the SVR-GO-BFS\({}_{n}\) model structures, this study uses MAPE as the primary criterion. In addition, the RMSE is used as GO's objective function. Prediction of the AOp values is the target of these models. The AOp database is split into training (80%) and test (20%) sets at random throughout the experiment's run. The training set of data is utilized to develop the forecasting model, while the testing data are utilized to evaluate the predictability. Importantly, all generated models receive the same training and test sets on a regular basis. Following building numerous models, it has been evidenced that as the number of iterations rises, the model computation time grows. Small population sizes, on the other hand, generate inconsistent fitness values. Therefore, multiple groups of 50, 100, 150, 200, and 250 population numbers in the optimization model were chosen for the purposes of the current study, and their iteration curves were made based on the right fitness values. Concerning the GO, the number of search agents was set as 40, as well as the largest iteration number of developed models was set as 100. Regarding SVR, the lower and upper bounds of \(\gamma\) and C were set to (0.01-50) and (0.01, 100). All inputs were normalized between zero and one for the estimation of performance criteria, as well as to decrease the calculation complications during searching for hyperparameters of models. Table 3 presents the best predictive fulfilment of the models developed in the current investigation. This table corresponds to SVR hyperparameter values optimized using GO and the smallest optimal collection of inputs. It can be seen that the model with seven inputs (SVR-GO-BFS\({}_{7}\)) achieved the highest accuracy and lowest errors. Figure 6 depicts the outcomes of fitness values for SVR-GO-BFS\({}_{7}\) models in forecasting AOp, along with their iteration counts. Furthermore, to minimize the GO's cost function, the RMSE was chosen. This figure shows that the best population size for SVR-GO-BFS\({}_{7}\) is 200. Sizable errors in prediction are improbable to have occurred. Only average alternations were adopted up to iteration number 65; following this, no significant difference in the RMSE values was indicated. It should be noted that all models achieved the minimum RMSE in less than 70 iterations, which shows the power of GO in optimizing the SVR hyperparameters. It was not required to have the full collection of significant inputs (n = nine) to obtain the most reliable predictive performance. Therefore, the authors can draw the conclusion that the effectiveness of SVR-GO-BFS\({}_{n}\) in forecasting the AOp is excellent. The performance of the SVR-GO-BFS\({}_{n}\) models based on various mixtures of significant inputs (n = 1 set to n = 9 sets) is presented in Figure 7 through the stacked area. In Figure 7, it is obvious that the MAPE, RMSE, and MAE estimates obtained from all the SVR-GO-BFS\({}_{n}\) models were essentially lower than 2.6953, 3.6637, and 3.3083, sequentially, even if only one input was added to the model. For instance, the achieved values of MAPE, RMSE, and MAE were 2.6953, 3.2241, and 2.8476, sequentially, if just one input is employed for the model creation. Furthermore, the SVR-GO-BFS\({}_{2}\) model is associated with the poorest performance. This model achieved 0.9209 for R\({}^{2}\), 36637 for RMSE, 3.1152 for MAPE, and 3.3083 for MAE.