The MVLR model was developed using XLSTAT software to test the selected model. The input variables for the other model are utilized as the estimators, while the OTE₂₀ is used as response parameters. The generated form of the MVLR model from training data is given in Equation (4), and the fitness and suitability of this developed form are checked through the test data. The outcomes in the agreement diagram for training and testing datasets as depicted in Figure 4. The estimated error and accuracy of the results are assessed by performance metrics (Table 3). Figure 4 presents a scatter plot of OTE data points at the gabion weir. Relatively, the bulk of the training data points for the training phase is near to zero error line (along the perfect line), while for the testing stage, the testing data points are slightly away from the very zero error line, which implies that the generated predictive model is not so precise and accurate. This contention is further buttressed from the perusal of Figure 5, where the variation of the computed data has been shown against the experimental values of the OTE₂₀ for the test and training datasets. Table 3 suggests that cc and rmse are 0.883 and 0.265, respectively, for the test dataset, which means the correlation is moderately good but high error as cc = 0.902 and rmse = 0.047 that of the training dataset. It suggests that this model performs well in the training and testing phases.