## 5 Results  
The results section presents a comprehensive comparison of model performance across different configurations of virtual sensors. The comparison of results between 15, 30, and 45 virtual sensors against the baseline of 0 virtual sensors is presented in the Results section (Tables 1-3). These three tables summarize the accuracy achieved when 15, 30, and 45 virtual sensors are added to the baseline dataset of 15 physical sensors. Each table highlights the percentage change in accuracy for various environmental categories, providing detailed insights into the impact of virtual sensor integration. These comparisonsare crucial for understanding the optimal number of virtual sensors required to maximize predictive accuracy while maintaining computational efficiency.  
These tables help visualize the effect of increasing the number of virtual sensors on our model's performance across different categories. The data demonstrate both significant improvements and some decreases in accuracy, indicating that the introduction of virtual sensors can be beneficial in certain categories but may not always lead to improvements in  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Category** & **0 VS** & **15 VS** & **\% Change** \\ \hline No Fire & 95.00\% & 55.00\% & \(-\)42.11\% \\ Lightning & 75.00\% & 100.00\% & 33.33\% \\ Thundering & 90.00\% & 95.00\% & 5.56\% \\ Smoke & 95.00\% & 100.00\% & 5.26\% \\ Fire about to Start & 90.00\% & 95.00\% & 5.56\% \\ Fire & 80.00\% & 35.00\% & \(-\)56.25\% \\ Fire Increasing & 65.00\% & 75.00\% & 15.38\% \\ Intensive Fire & 70.00\% & 90.00\% & 28.57\% \\ Fire with Thundering and Lightning & 85.00\% & 95.00\% & 11.76\% \\ Fire Decreasing & 65.00\% & 80.00\% & 23.08\% \\ Rain Started and Fire stopped & 65.00\% & 85.00\% & 30.77\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of accuracy: 0 vs. 15 virtual sensors.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Category** & **0 VS** & **30 VS** & **\% Change** \\ \hline No Fire & 95.00\% & 95.00\% & 0.00\% \\ Lightning & 75.00\% & 85.00\% & 13.33\% \\ Thundering & 90.00\% & 90.00\% & 0.00\% \\ Smoke & 95.00\% & 85.00\% & \(-\)10.53\% \\ Fire about to Start & 90.00\% & 85.00\% & \(-\)5.56\% \\ Fire & 80.00\% & 75.00\% & \(-\)6.25\% \\ Fire Increasing & 65.00\% & 95.00\% & 46.15\% \\ Intensive Fire & 70.00\% & 90.00\% & 28.57\% \\ Fire with Thundering & 85.00\% & 60.00\% & \(-\)29.41\% \\ Fire with Thundering and Lightning & 65.00\% & 80.00\% & 23.08\% \\ Fire Decreasing & 80.00\% & 80.00\% & 0.00\% \\ Rain Started and Fire Stopped & 65.00\% & 90.00\% & 38.46\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of accuracy: 0 vs. 30 virtual sensors.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Category** & **0 VS** & **45 VS** & **\% Change** \\ \hline No Fire & 95.00\% & 90.00\% & \(-\)5.26\% \\ Lightning & 75.00\% & 90.00\% & 20.00\% \\ Thundering & 90.00\% & 85.00\% & \(-\)5.56\% \\ Smoke & 95.00\% & 90.00\% & \(-\)5.26\% \\ Fire about to Start & 90.00\% & 100.00\% & 11.11\% \\ Fire & 80.00\% & 80.00\% & 0.00\% \\ Fire Increasing & 65.00\% & 85.00\% & 30.77\% \\ Intensive Fire & 70.00\% & 80.00\% & 14.29\% \\ Fire with Thundering & 85.00\% & 100.00\% & 17.65\% \\ Fire with Thundering and Lightning & 65.00\% & 90.00\% & 38.46\% \\ Fire Decreasing & 80.00\% & 70.00\% & \(-\)12.50\% \\ Rain Started and Fire stopped & 65.00\% & 85.00\% & 30.77\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of accuracy: 0 vs. 45 virtual sensors.  
all cases. Further exploration with higher numbers of virtual sensors could help refine the model and achieve better performance across all categories.