## 4 Results
### Explainable AI Model  
PDP is usually presented as a two-dimensional graph. The X-axis represents the values of the examined feature, and the Y-axis represents the average output predicted by the model [63; 64]. In this study, the prediction was performed using the decision tree algorithm with PDP, one of the XAI techniques. The graph shows that the cycle feature has almost no effect on the model predictions according to PDP (Figure 11). Even if this feature changes, there is no change in the model's prediction values. In the graph, the effect of the current (A) feature on the model predictions is more pronounced. As the Current(A) value decreases (increases in the negative direction), it is seen that the model's prediction values also decrease. It shows that the current (A) feature significantly affects the model predictions. The prediction was performed using the decision tree algorithm with PDP, one of the XAI techniques. The above graph in Figure 12 shows the effects of Cycle and Current(A) features on model predictions by showing PDP. The effect of the Current(A) feature on model predictions is quite evident. As the value of the Current(A) \(-4\) direction decreases (increases in a negative direction), the model's predictions decrease gradually. It shows that the Current (A) feature significantly affects the model. The graph shows more changes \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MAE** & **RMSE** & **R\({}^{2}\) Score** \\ \hline CNN & 0.00065643034 & 0.00109313993 & 0.884453491 \\ \hline RNN & 0.00058616823 & 0.00063540572 & 0.960960203 \\ \hline CNN + RNN & 0.00056917915 & 0.00060882634 & 0.964158004 \\ \hline Ensemble & 0.00030177282 & 0.00044389487 & 0.980946903 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Drone 129 MAE, RMSE, R\({}^{2}\) Score Performance Metrics. Figure 11: Results for Drone 92 Partial Dependence Plots using XAI Model. when the Current (A) value is around \(-4\), which may indicate that the model is more sensitive in this range. The model's predictions also decrease as the Current(A) value decreases. It indicates that the Current(A) feature is a critical variable for the model. However, it is observed that the Cycle feature has almost no effect on model predictions. Regarding sensitivity analysis for PDP results for Drone 92 and 129, the cycle variable is insensitive to the model's prediction performance. In other words, the model makes little or no update in the predictions based on this variable. On the other hand, according to the sensitivity analysis, the current variable is highly sensitive to the model's predictions and significantly affects the prediction performance. Figure 12 shows some fluctuations, indicating that the Current variable has a more sensitive effect on the model at certain intervals. In particular, there is an increase in sensitivity between \(-4\) and \(-3\), after which the effect tends towards a more balanced decrease. The proposed method has several strengths but also some limitations. First, the complexity of the CNN-RNN-based hybrid model can lead to high computational costs and long training times, especially for large datasets. In addition, the performance of the model is highly dependent on the quality and quantity of the data. Missing or noisy data can have a negative impact on the accuracy of the prediction of battery behavior, especially during long-duration flights. The model may not be able to respond immediately to dynamic flight conditions, such as sudden environmental changes or load variations, due to limited real-time adaptation. In addition, XAI techniques help to make the model's decision processes understandable. However, they may not be sufficient to fully explain complex interactions, especially in multivariate scenarios. On the other hand, by increasing the accuracy of battery performance prediction, this method provides significant success in predicting critical metrics such as remaining life and state of charge.