## 2 Materials and Methods
### Adop Fuzzy AHP Methods for the Forest Fire Risk Assessment and Outbreak Detection  
The adopted fuzzy AHP method, as discussed in Section 4.1, is based on the measuring the meteorological parameters (temperature, relative humidity, wind speed, and rainfall) and the concentrations of the chemical gases (oxygen, carbon monoxide, and carbon dioxide) that are responsible for forest fire ignition. This real-time environmental monitoring of the aforementioned forest fire risk factors is performed by a distributed and IoT-based sensor network. Corresponding sensors are deployed in the given area, with the decreasing node density as the forest fire susceptibility decreases, considering the results of analysis with the fuzzy AHP method presented in Section 3. The sensor node location is considered known due to planned deployment (i.e., the information could be written in sensor node software, or the unique sensor node ID could be defined with the corresponding location information written in the system database). Alternatively, each sensor node can be equipped with a Global Positioning System (GPS) module, which would increase the power consumption and cost of the sensor node. Each sensor periodically collects the measured data and employs the fuzzy logic Mamdani inference system [57; 58] that has been adopted to analyze the complex environmental changes. Seven input variables measured by sensors are fuzzified using the four membership functions shown in Figures 2 and 3. Different fuzzy sets are defined for each linguistic variable expressed as \"very low\", \"low\", \"normal\", \"high\", and \"extreme\", which is presented in Table 3. \begin{table}
\begin{tabular}{c c} \hline \hline
**Performance Index Value** & **Forest Fire Susceptibility Zone** \\ \hline
0.0–0.2 & Very high \\
0.2–0.4 & High \\
0.4–0.6 & Medium \\
0.6–0.8 & Low \\
0.8–1.0 & Very low \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Forest fire susceptibility zones classification according to performance index values. Figure 2: Membership functions of meteorological variables, adopted from [57]. The adopted fuzzy sets for the forest fire risk are designed according to the rule of 30, which is considered as a relevant preventive model of forest fire risk [57]. This rule considers measurements of temperature and wind speed above 30 \({}^{\circ}\)C and 30 km/h, respectively, with the humidity values below 30%, as the environmental conditions that favor the forest fires' occurrence. The adopted fuzzy sets for the fire outbreak occurrence are proposed and designed, [57], based on the occurrence of the unusual increases in carbon monoxide and carbon dioxide levels above their typical environmental concentrations at forest areas, at the same time as the occurrence of the unexpected oxygen level decrease below 21%. Every measured environmental variable is compared with its average value. The number of consecutive measurements used to calculate this average value depends on the current environmental conditions as summarized in Table 4. A comparison between the last environmental measurement and its average is necessary to evaluate unusual environmental changes and predict fire occurrence.