## 4 Results
### CNN + RNN Hybrid Model  
The first graph in Figure 8 shows the changes in the MAE and loss metrics during the training process of the CNN + RNN model. Training MAE shows the model's prediction error on the training set. When the graph is examined, the training MAE has decreased rapidly since the first epoch. It shows that the model has adapted well to the training set. In the second graph, the training loss decreases rapidly in the first few epochs and then approaches almost zero. It shows that the model has adapted quickly to the training set and has been successful in learning.  
In the first graph in Figure 9, the training MAE shows a significant decrease in the first epoch and stabilizes at a low level. The validation MAE moves in line with the training MAE and remains at a low value. In the second graph, the training loss decreases rapidly, and the validation loss similarly stabilizes at a low level.  
The model trained with Drone No: 92 predicted and actual values closely match with the R\({}^{2}\) score of 0.99. The data is almost perfectly aligned at 45 degrees (dashed line, Figure 10). It shows that the model's predictive power is very high, which can predict the actual values.  
Figure 8: The results of the CNN + RNN Model (Drone number 92).  
Figure 7: Recurrent Neural Networks (RNNs) Model MAE and Loss values (Drone 129).  
There is also a strong agreement between the model's predicted and actual values, as can be seen from the R\({}^{2}\) score of 0.98 trained with Drone No: 129. However, compared to the left graph in Figure 10, there is a little more spread in this graph. It indicates that the model's performance is slightly lower but has relatively solid predictive power overall.  
In drone data number 92, the deep ensemble learning model had a 0.0003267631 MAE, with an R\({}^{2}\) value of 0.986658468, and generally exhibited the best prediction performance compared to other model comparisons. The CNN + RNN hybrid model exhibited a high RMSE performance of 0.0004640816. RNN and CNN models exhibited similar performance, but CNN had the lowest prediction performance among these four models (Table 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MAE** & **RMSE** & **R\({}^{2}\) Score** \\ \hline CNN & 0.000608860114 & 0.002089417079 & 0.954199043 \\ \hline RNN & 0.000513610452 & 0.00076047798 & 0.962692018 \\ \hline CNN + RNN & 0.000359522800 & 0.000464081626 & 0.985894800 \\ \hline Ensemble & 0.000326763109 & 0.000676465770 & 0.98665846 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Drone 92 MAE, RMSE, R\({}^{2}\) Score Performance Metrics.  
Figure 10: The results for the Drone 92 and Drone 129 datasets of Deep Ensemble Learning Model R\({}^{2}\) Scores.  
Figure 9: CNN + RNN Model MAE and Loss values (Drone number 129).  
In the data of drone number 129, the deep ensemble learning model generally exhibited the best prediction performance compared to other model comparisons with a 0.00030177 MAE, 0.980946 R\({}^{2}\), and 0.00044389 RMSE value. The CNN + RNN hybrid model exhibited high performance in terms of RMSE performance. RNN and CNN models exhibited similar performance, but CNN had the lowest prediction performance among these four models (Table 4).