# Predicting the Duration of Forest Fires Using Machine Learning Methods
## 3 Results  
The experiments were conducted using the freely available programming tool of WEKA [115]. The software, which is written in the JAVA programming language, to be portable, can be downloaded freely from [[https://ml.cms.waikato.ac.nz/weka/](https://ml.cms.waikato.ac.nz/weka/)]([https://ml.cms.waikato.ac.nz/weka/](https://ml.cms.waikato.ac.nz/weka/)) (accessed on 14 September 2024), or it can be found in the repositories of most Linux systems. The WEKA software is a collection of machine learning and data analysis tools and it also contains some visualization tools for modeling. WEKA has been used with success in many cases, such as educational problems [116; 117], medical problems [118; 119], etc. The validation of the conducted experiments was performed using the ten-fold cross validation technique. The experiments were carried out on an AMD Ryzen 5950X installed at the University of Ioannina, with 128 GB of RAM, running the Debian Linux operating system. The experimental results using the methods mentioned in the previous section and the 10 modified datasets from the Hellenic Fire Service are listed in Table 1. The following applies to the tables of experimental results:  
1. The numbers in cells denote the average classification error as calculated on the test set. 2. The column Year denotes the year where the machine learning methods were applied. 3. The column BAYESNET stands for the application of the Bayesian network method. 4. The column NAIVEBAYES denotes the application of the naive Bayes algorithm. 5. The column LOGISTIC represents the application of the logistic regression algorithm. 6. The column MLP denotes the application of a neural network to the dataset. 7. The column J48 denotes the application of the J48 method to the forest fire data. 8. The column RANDOMFOREST denotes the usage of the Random Forest method on the data. 9. The row Average denotes the average classification error for all datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Year** & **BAYESNET** & **NAIVEBAYES** & **LOGISTIC** & **MLP** & **J48** & **RANDOMFOREST** \\ \hline
2014 & 11.44\% & 12.89\% & 9.81\% & 11.37\% & 10.04\% & 9.42\% \\ \hline
2015 & 11.08\% & 11.26\% & 9.53\% & 10.65\% & 9.51\% & 8.95\% \\ \hline
2016 & 25.71\% & 13.00\% & 3.41\% & 3.90\% & 3.65\% & 3.00\% \\ \hline
2017 & 11.04\% & 11.51\% & 9.48\% & 10.08\% & 10.30\% & 9.29\% \\ \hline
2018 & 11.20\% & 10.46\% & 9.09\% & 9.48\% & 9.27\% & 8.58\% \\ \hline
2019 & 9.61\% & 9.25\% & 8.29\% & 8.53\% & 9.08\% & 8.01\% \\ \hline
2020 & 18.00\% & 6.72\% & 5.54\% & 5.97\% & 6.09\% & 5.50\% \\ \hline
2021 & 12.35\% & 14.15\% & 12.04\% & 13.59\% & 13.59\% & 11.92\% \\ \hline
2022 & 10.25\% & 9.62\% & 9.01\% & 9.47\% & 9.04\% & 8.93\% \\ \hline
2023 & 9.74\% & 9.19\% & 8.26\% & 8.77\% & 8.39\% & 7.66\% \\ \hline
**Average** & **13.04\%** & **10.81\%** & **8.45\%** & **9.18\%** & **8.90\%** & **8.13\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results using various machine learning models for 10 years of observations. The numbers in cells denote average classification error as measured on the test set. Judging from the experimental results, it is evident that the Random Forest technique outperforms the others along with the logistic regression technique. This observation is reinforced in the box plot of Figure 5.  
Also, the precision and recall measures for every dataset and for each method are presented in Table 2.  
A statistical comparison of Random Forest with the other machine learning methods is depicted in Figure 6. In this figure pairwise statistical comparisons of the models are made using the Kruskal-Wallis test, and the results are presented with asterisks instead of the exact \(p\)-values. The asterisks indicate the level of statistical significance for the comparisons. Each number of asterisks corresponds to a different \(p\)-value threshold, showing how likely it is to reject the null hypothesis (that there is no difference between the groups) based on the data.