# Predicting the Duration of Forest Fires Using Machine Learning Methods
## 3 Results
These levels are usually as follows: One asterisk (*) represents a \(p\)-value less \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & \\ \hline
2014 & 0.889 & 0.886 & 0.851 & 0.871 & 0.89 & 0.902 & 0.87 & 0.886 & 0.888 & 0.90 & 0.898 & 0.906 \\ \hline
2015 & 0.892 & 0.889 & 0.87 & 0.887 & 0.891 & 0.905 & 0.876 & 0.893 & 0.892 & 0.905 & 0.901 & 0.91 \\ \hline
2016 & 0.959 & 0.743 & 0.959 & 0.87 & 0.96 & 0.966 & 0.955 & 0.961 & 0.959 & 0.963 & 0.968 & 0.97 \\ \hline
2017 & 0.897 & 0.89 & 0.869 & 0.885 & 0.893 & 0.905 & 0.886 & 0.889 & 0.886 & 0.897 & 0.899 & 0.907 \\ \hline
2018 & 0.897 & 0.888 & 0.879 & 0.895 & 0.894 & 0.909 & 0.89 & 0.905 & 0.894 & 0.907 & 0.905 & 0.914 \\ \hline
2019 & 0.914 & 0.904 & 0.894 & 0.907 & 0.903 & 0.917 & 0.903 & 0.915 & 0.898 & 0.909 & 0.912 & 0.92 \\ \hline
2020 & 0.929 & 0.82 & 0.923 & 0.933 & 0.937 & 0.945 & 0.933 & 0.94 & 0.931 & 0.939 & 0.94 & 0.945 \\ \hline
2021 & 0.879 & 0.876 & 0.835 & 0.858 & 0.865 & 0.88 & 0.846 & 0.864 & 0.849 & 0.864 & 0.871 & 0.881 \\ \hline
2022 & 0.91 & 0.897 & 0.889 & 0.904 & 0.893 & 0.91 & 0.891 & 0.905 & 0.896 & 0.91 & 0.9 & 0.911 \\ \hline
2023 & 0.912 & 0.903 & 0.894 & 0.908 & 0.905 & 0.917 & 0.899 & 0.912 & 0.906 & 0.916 & 0.923 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Precision and recall for every machine learning method. Figure 5: Box plot for the used machine learning techniques. than 0.05, indicating a significant difference at the 5% level, which is often considered an acceptable threshold for statistical significance. Two asterisks (**) represent a \(p\)-value less than 0.01, indicating a significant difference at the 1% level, which is a stronger indication for rejecting the null hypothesis. Three asterisks (***) represent a \(p\)-value less than 0.001, indicating a very strong level of statistical significance, with an extremely small likelihood that the difference is due to random chance. The Kruskal-Wallis test was used because we had many groups to compare, specifically, six different tree counts, making it necessary to use a test that could compare more than two groups simultaneously. In the statistical visualization of Figure 6, the Bayes Net model shows higher error rates in most years, with an exceptionally high error in 2016, reaching 25.71%. However, in other years, such as 2019 and 2023, its performance was quite close to RANDOMFOREST, although the latter remains slightly better. Although NAIVEBAYES performs relatively well in several years, it still shows higher errors compared to RANDOMFOREST, especially during the 2020-2021 period, where NAIVEBAYES  
Figure 6: Statistical comparison between the Random Forest method and the other machine learning methods. had significantly increased errors. LOGISTIC records exceptionally low error rates in 2016, but remains consistently below RANDOMFOREST in most years. An exception is in 2020, where a divergence is observed, with better performance for RANDOMFOREST. MLP shows some fluctuations in errors, with significant improvement after 2016. Despite better performance in certain years, such as 2021, it remains generally inferior compared to RANDOMFOREST. J48 has fairly comparable error rates with RANDOMFOREST, especially after 2018, but RANDOMFOREST consistently proves to be the most efficient model in most years. RANDOMFOREST consistently emerges as the best model based on error rates, recording lower errors in most years compared to other machine learning models. Although other models, such as LOGISTIC and MLP, perform well in certain years, RANDOMFOREST maintains a more stable and reliable performance with fewer fluctuations. Furthermore, in order to evaluate the performance and the effectiveness of the Random Forest technique, an additional test was carried out where the number of trees for this method increased from 10 to 200.