# Forest Fire Risk Forecasting with the Aid of Case-Based Reasoning
## 4 Discussion and Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Work/Criteria** & **Area Name** & **Area Features** & \begin{tabular}{c} **The Main Fire** \\ **Reason** \\ \end{tabular} & \begin{tabular}{c} **The Volume of the** \\ **Case-Base or Training** \\ **Sample** \\ \end{tabular} & \
\begin{tabular}{c} **Number of Case** \\ **Properties or** \\ **Factors** \\ \end{tabular} & Technique &
\begin{tabular}{c} **The Accuracy or** \\ **Prediction Score** \\ \end{tabular} \\ \hline
**Our research** & \begin{tabular}{c} **Ikutsk Oblast** \\ **(Russia)** \\ \end{tabular} & \
\begin{tabular}{c} **Low population density;** \\ **(Russia)** \\ \end{tabular} &
\begin{tabular}{c} **Weather and forest** \\ **condition** \\ \end{tabular} & **2240** & **41 (8/2) * & **CBR** & **0.51** \\ \hline
[4] & \begin{tabular}{c} DaXingAnMountains \\ (China) \\ \end{tabular} & \begin{tabular}{c} Hard-to-reach places; a small \\ number of weather stations \\ \end{tabular} & \
\begin{tabular}{c} Careless handling of \\ fire when visiting the \\ forest; lightning (dry \\ thunderstorms) \\ \end{tabular} & 560 & 5 \(\,\)b &
\begin{tabular}{c} CBR \\ \end{tabular} & 0.688 \\ \hline
[45] & \begin{tabular}{c} Mediterranean areas \\ \end{tabular} & - & - & 2000 & \
\begin{tabular}{c}
\begin{tabular}{c} Radial Basis Function \\ (RBF) Network \\ CBR \\ \end{tabular} & 0.52 \\ \hline
[14] & \begin{tabular}{c} Noshahr forests \\ (Iran) \\ \end{tabular} & \begin{tabular}{c} A popular tourist destination \\ \end{tabular} & \
\begin{tabular}{c} Careless handling of \\ fire when visiting \\ the forest \\ \end{tabular} & 30 & 10 &
\begin{tabular}{c} Analytical \\ Network/Process \\ (ANP) and Fuzzy \\ Logic (FL) \\ \end{tabular} & 0.819 \\ \hline
[6] & \begin{tabular}{c} Huelchang County \\ (China) \\ \end{tabular} & \
\begin{tabular}{c} Long fine season; the presence \\ of meadows \\ \end{tabular} & - & 244 (122 + 122) 4 & 10 &
\begin{tabular}{c} Weights-of-Evidence \\ (WORE) model and a \\ knowledge-based \\ Analydellizationary \\ Process (AHP) \\ Logistic regression \\ \end{tabular} & 0.91 \\ \hline
[47] & \begin{tabular}{c} Minudash Forests \\ (Iran) \\ \end{tabular} & \
\begin{tabular}{c} Tropical dry forests is a \\ overexposed vegetation type \\ \end{tabular} & - & 151 & 7 &
\begin{tabular}{c} Modified AMP \\ Mamani FL \\ \end{tabular} & 0.77 \\ \hline
[8] & \begin{tabular}{c} Pakistan \\ \end{tabular} & \
\begin{tabular}{c} Arrid climate \\ \end{tabular} & 44,622 (22,311 + 22,311) * & 34 &
\begin{tabular}{c} Machine learning \\ (8 algorithms) \\ \end{tabular} & 0.577-0.852 \\ \hline \hline \end{tabular}  
* 38 properties in the identifying part and 2 properties in the training part.b
* three dynamic and two static calculated indexes were used to describe the cases: 10 properties in the identifying part and 10 properties in the training part (the same properties but with the values of the prediction step).
* 122 recent fires + 122 random no-fire points from the fire-free areas.
* 22,311 fine points + 22,311 no-fire points.  
\end{table} 
Table 4: A comparison with existing approaches/methods. From the first point of view, the proposed use of decision tables and PKBD contributes to the involvement of end users in the development process and ensures the creation of prototypes of knowledge bases and their debugging without involving programmers. According to the estimates of the effectiveness of this approach for rule-based knowledge bases from [68], the use of decision tables can reduce the time spent compared to hand-coding by an average of 52%. From the second point of view, the use of CBR allows one to use domain models (Figure 5), providing their mapping into the structures of the knowledge base, and also makes the decision-making process \"transparent\" since the mechanisms of forming the similarity are known, and when it is detailed, it is possible to determine variables of greater importance for a certain situation. Precise and timely forecasting of forest fire risks provides effective decision support and situational control.