# Predicting the Duration of Forest Fires Using Machine Learning Methods
## 4 Conclusions  
In the present research work, a study was made of the duration of forest fires using open data for the Greek area. These data contained information such as the area of the fire, the time it erupted, the destruction it caused, as well as the human resources involved in extinguishing it. Machine learning models were then used to estimate the duration of the fires. The successful prediction of fire duration contributes to the proper management of such catastrophic events by state mechanisms. The machine learning models used included models such as artificial neural networks, decision trees, etc. Most of the machine learning techniques used achieved significantly low error values for each year of experimental data. On average, these classification error values were in the range of 8-13% with the Random Forest technique achieving the lowest value. Furthermore, additional tests were executed to measure the effectiveness of Random Forest and neural networks. In addition, the stability of the used data was assessed in the presence of random noise with encouraging results.  
The present work could be extended in the future in various research directions, as follows:  
1. Incorporation of more machine learning methods from the relevant literature.
2. Using feature selection or construction techniques from the recent literature to identify the most important factors influencing the classification process.
3. Usage of methods that create classification rules, in order to discover any hidden relationships between the data and the classes of the datasets.
4. Usage of data that also include meteorological data, in order to identify a possible correlation of the categories with the meteorological conditions that prevailed at the time of the fire.
5. Parallel programming techniques may be incorporated to speed up the optimization process, such as MPI [125] or the OpenMP library [126].  
C.K., V.C. and I.G.T. conceived of the idea and the methodology, and C.K. and V.C. implemented the corresponding software. C.K. conducted the experiments, employing objective functions as test cases, and provided the comparative experiments. A.S. performed the necessary statistical tests. All authors have read and agreed to the published version of the manuscript.  
This research has been financed by the European Union: Next Generation EU through the Program Greece 2.0 National Recovery and Resilience Plan, under the call RESEARCH-CREATE-INNOVATE, project name \"iCREW: Intelligent small craft simulator for advanced crew training using Virtual Reality techniques\" (project code: TAEDK-06195).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Year** & **MLP2 BFGS** & **MLP5 BFGS** & **MLP10\_BFGS** & **MLP15\_BFGS** & **MLP20 BFGS** \\ \hline
2014 & 12.88\% & 12.40\% & 11.61\% & 11.58\% & 11.43\% \\ \hline
2015 & 11.78\% & 10.98\% & 10.86\% & 12.71\% & 12.66\% \\ \hline
2016 & 5.08\% & 4.40\% & 4.36\% & 4.13\% & 4.14\% \\ \hline
2017 & 12.14\% & 10.71\% & 10.60\% & 10.80\% & 10.46\% \\ \hline
2018 & 10.46\% & 10.28\% & 10.16\% & 10.04\% & 9.90\% \\ \hline
2019 & 9.61\% & 9.21\% & 9.27\% & 9.12\% & 9.01\% \\ \hline
2020 & 7.49\% & 6.67\% & 6.69\% & 6.36\% & 6.40\% \\ \hline
2021 & 16.29\% & 14.82\% & 14.12\% & 14.05\% & 13.95\% \\ \hline
2022 & 10.39\% & 10.06\% & 9.86\% & 9.67\% & 9.63\% \\ \hline
2023 & 10.51\% & 9.96\% & 9.55\% & 9.45\% & 9.47\% \\ \hline
**Average** & **10.66\%** & **9.95\%** & **9.71\%** & **9.79\%** & **9.71\%** \\ \hline \end{tabular}
\end{table}
Table 5: Experiments with different numbers of processing nodes for the artificial neural network case. The BFGS optimization method was used to train the neural network.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.