## 3 Results and Discussions
### Comparative Assessment of DTR and GBR during the Forecasting Process  
The results shown in Figure 6 corroborate that the testing, training, and validation of the GBR and DTR models for the predictions of the sorption and desorption of the BNF were successful. However, the performance of the models is accessed based on the results obtained from the evaluation metrics. Figure 5: Sorption and desorption behavior of: (**a**) untreated, (**b**) treated after 1 h, (**c**) treated after 24 h, (**d**) treated after 48 h, and (**e**) treated after 72 h.  
Figure 6: _Cont._The results shown in Figure 7 represent the various evaluation metrics obtained from the GBR and DTR for the prediction of the sorption and desorption levels of the BNF. The Mean Absolute Errors were equal to 0.072 and 0.133 for the GBR and DTR. This signifies that both models indicated good prediction with a low MAE. Nevertheless, GBR displayed a lower MAE (0.072), indicating very good accuracy because a lower MAE is better. Therefore, GBR outperformed DTR in terms of the MAE metric during the prediction of the sorption level in the BNF. Meanwhile, during the desorption level prediction, both GBR and DTR depicted low MAE values of 0.059 and 0.057, respectively. Comparing the MAE of DTR during the prediction of the sorption and desorption level, there is a decrease from 0.133 to 0.057, indicating that DTR performed better when predicting the desorption compared to the sorption level's prediction. The value of MAE varied from 0 to 0.08 when a tree-based model was used to predict the compressive strength of concrete [62]. These values are close to the present results. Nonetheless, there is an investigation where GBR and DT exhibited an MAE of 8.65 and 8.94, respectively [32]. That shows the significant difference in terms of the MAE results with our present work. The difference during these investigations was that different types of data were used (primary in our case and secondary dataset in the previous work), with different materials both conventional and unconventional (concrete in the previous study and natural fiber in the present). The MSE for GBR and DTR demonstrated values of 0.04 and 0.114, respectively, during the sorption level's prediction. The MSE measures the average of the squares of the errors, meaning the difference between the estimated values and the actual value [33]. Furthermore, an MSE of 0.04 for GBR confirms the small size of the errors for this model's predictions of the sorption level. An MSE of 0.114 indicates that the squared errors are low for the DTR, contributing to the model's effective performance. However, this value is higher than the one observed in GBR. The value of MSE is like the results obtained during the prediction of the photochemical ozone creation potential for the Life Cycle Assessment (LCA) [63]. However, during the prediction of the desorption level in the BNF, both models displayed low MSE 0.012 and 0.013 for GBR and DTR, respectively, which is another indication that the models are making good predictions. It is important to note that MSE penalizes large errors more than MAE, so a very low MSE suggests that the models are making predictions of the desorption close to the actual values of the desorption level in the BNF [33]. Subsequently, GBR displayed higher performance against DTR in terms of MSE evaluation during the prediction of the sorption level in the BNF. Figure 6: Prediction vs. experimental using DTR for: (**a**) sorption level in the BNF, (**b**) desorption level in the BNF. Prediction vs. experimental using GBR for: (**c**) sorption level in the BNF, (**d**) desorption level in the BNF. Gradient boosting regressor and DTR models revealed an RMSE of 0.201 and 0.338, respectively, during the sorption level's prediction. The RMSE provides a measure of how accurately the model predicts the sorption characteristics of the BNF [30]. An RMSE of 0.201 for the GBR indicates that the model's predictions are quite similar to the actual values of the sorption, with a small average deviation.