# Multi-Sensor Photoelectric Fire Alarm Device Implementation for Early Fire Detection in Campsites
## 3 Multi-Sensor Photoelectric Fire Detection System
### Relationship Between Weather and Fire Occurrence
#### 3.1.2 The Relationship Model Between Fire Occurrence and Temperature and Humidity  
In this study, the extracted and merged data were categorized into intervals based on temperature and humidity. The temperature was segmented into approximately \(5^{\circ}\) intervals ranging from \(-40\)\({}^{\circ}\)C to \(+40\)\({}^{\circ}\)C, and the humidity into approximately 5% intervals ranging from 15% to 90%. The number of fire occurrences in each interval was then counted. Similarly, the AWS data were categorized based on the frequency of occurrence within the same temperature and humidity range. The AWS dataset covers 109 regions, and temperature and humidity were simultaneously recorded. To account for the differences among the datasets, the number of fire occurrences was multiplied by 109, and this adjusted figure was then divided by the frequency of occurrences in the AWS data for each temperature and humidity range to calculate the fire occurrence rate. The data preprocessing included several steps. First, data points with abnormally low fire occurrence rates at specific temperatures and humidity levels, which could represent outliers, were removed using the Interquartile Range (IQR) method [27]. The regions where the AWS did not measure humidity were also excluded. After these adjustments, the dataset was reduced to 24,412 preprocessed data points. Next, temperature was restricted to the range of \(-40\)\({}^{\circ}\)C to \(+40\)\({}^{\circ}\)C and binned into \(5^{\circ}\) intervals, while humidity was restricted to 20-90% and binned into 5% intervals. The number of fire occurrences within the categorized ranges was calculated, and the frequency  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Date of Fire Outbreak** & **City** & **District** & **Ignition Factor** \\ \hline
2020-01-01 0:00 & Seoul & Guro-gu & Careless \\ \hline
2020-01-01 0:05 & Gwangju & Gwangsan-gu & Careless \\ \hline
2020-01-01 0:06 & Gwangju & Gwangsan-gu & Unknown \\ \hline
2020-01-01 0:07 & Gyeonggi & Yeoju-si & Electrical factor \\ \hline
2020-01-01 0:12 & Gyeonggi & Yangpyeong-gun & Electrical factor \\ \hline
2020-01-01 0:21 & Incheon & Michuol-gu & Unknown \\ \hline
2020-01-01 0:43 & Gwangju & Buk-gu & Careless \\ \hline
2020-01-01 0:57 & Incheon & Ganghwa-gun & Careless \\ \hline
2020-01-01 1:12 & Gyeongsangdo & Changnyeong-gun & Electrical factor \\ \hline
2020-01-01 1:15 & Seoul & Gannam-gu & Unknown \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fire occurrence data from the National Fire Information System.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Date of Fire Outbreak** & **City** & **District** & **Ignition Factor** & **Location** & **Temperature (\({}^{\circ}\)C)** & **Humidity (\%)** \\ \hline
2021-01-02 9:00 & Daegu & Sinam & Electrical factor & 860 & \(-0.9\) & 65 \\ \hline
2021-01-02 11:00 & Daegu & Sinam & Mechanical factor & 860 & 0.3 & 42 \\ \hline
2021-01-02 14:00 & Daegu & Dalseon & Unknown & 828 & 3.5 & 29 \\ \hline
2021-01-02 14:00 & Daegu & Sinam & Electrical factor & 860 & 1.6 & 30 \\ \hline
2021-01-02 18:00 & Daegu & Sinam & arson & 860 & \(-0.7\) & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sample of merged data.