# Predicting the Duration of Forest Fires Using Machine Learning Methods
## 3 Results
The experimental results are outlined in Table 3.  
Also, a statistical comparison for the previously mentioned results is shown in Figure 7.  
In Table 3, it is observed that as the number of trees increases, the error decreases. This is expected in Random Forest models, as more trees typically improve the model's accuracy. For example, in 2014, the error decreases from 10.0477% for 10 trees to 9.253% for 200 trees, while in 2023, the error starts at 8.4684% for 10 trees and decreases to 7.4303% for 200 trees. In Figure 7, the paired comparisons using the t-test shows that for certain pairs, such as between 10 and 20 trees, the differences are statistically significant, with \(p\)-values less than 0.05. This indicates that increasing from 10 to 20 trees results in a significant reduction in error. On the other hand, for larger numbers of trees, such as 150 and 200, the \(p\)-values are greater than 0.05, suggesting that the differences in errors are not statistically significant. This means that increasing the number of trees beyond a certain point (e.g., from 150 to 200) does not have a substantial impact on reducing the error. In conclusion, the analysis shows that increasing the number of trees in the Random Forest model leads to a reduction in error, especially for smaller tree counts, where the differences are statistically significant (\(p<0.05\)). For larger tree counts, the differences in errors become smaller, and the \(p\)-values indicate that these differences are not statistically significant (\(p>0.05\)). In addition, to validate the experimental results, another comparison was made in which the Random Forest method participated as well as machine learning models, for the training of which the OPTIMUS optimization software was used. This software is freely available from [[https://github.com/itsoulos/GlobalOptimus/](https://github.com/itsoulos/GlobalOptimus/)]([https://github.com/itsoulos/GlobalOptimus/](https://github.com/itsoulos/GlobalOptimus/)) (accessed on 3 October 2024). The experimental results for this comparison are outlined in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Year** & **10 Trees** & **20 Trees** & **50 Trees** & **100 Trees** & **150 Trees** & **200 Trees** \\ \hline
2014 & 10.05\% & 9.66\% & 9.59\% & 9.43\% & 9.24\% &  
The following notation is used:  
1. The column RANDOM FOREST denotes the results with the method Random Forest implemented by the WEKA package. 2. The column SVM denotes the application of the support vector machine (SVM) method [120] using the libsvm software package [121]. 3. The column MLP10 BFGS stands for the results obtained by an artificial neural network trained by the BFGS optimization method, as modified by [PERSON] [122]. This neural network is equipped with 10 processing nodes. 4. The column MLP10 LBFGS represents the results obtained by a neural network with 10 processing nodes that was trained using the limited memory BFGS optimization method [123]. 5. The column RBF10 denotes the results produced by the training of a radial basis function (RBF) [124] network with 10 processing nodes. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **Random Forest** & **SVM** & **MLP10\_BFGS** & **MLP10\_LBFGS** & **RBF10** \\ \hline
2014 & 9.43\% & 14.19\% & 11.61\% & 10.28\% & 13.76\% \\ \hline
2015 & 8.95\% & 13.08\% & 10.86\% & 9.71\% & 12.34\% \\ \hline
2016 & 3.00\% & 5.03\% & 4.36\% & 3.61\% & 7.25\% \\ \hline
2017 & 9.30\% & 13.20\% & 10.60\% & 9.77\% & 12.87\% \\ \hline
2018 & 8.58\% & 11.50\% & 10.16\% & 9.15\% & 11.12\% \\ \hline
2019 & 8.02\% & 10.67\% & 9.27\% & 8.26\% & 9.83\% \\ \hline
2020 & 5.51\% & 8.18\% & 6.69\% & 5.62\% & 10.39\% \\ \hline
2021 & 11.92\% & 17.98\% & 14.12\% & 12.71\% & 18.17\% \\ \hline
2022 & 8.94\% & 11.16\% & 9.86\% & 8.98\% & 11.05\% \\ \hline
2023 & 7.67\% & 11.37\% & 9.55\% & 8.63\% & 11.53\% \\ \hline
**Average** & **8.13\%** & **11.64\%** & **9.71\%** & **8.67\%** & **11.83\%** \\ \hline \hline \end{tabular}
\end{table}