# Fire Risk Assessment Models Using Statistical Machine Learning and Optimized Risk Indexing
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Accuracy** \\ \hline KFRI & 0.5384 \\ \hline Logistic regression & 0.5552 \\ \hline DNN & 0.7514 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Prediction accuracy comparison. After calculating NKFRI, \"PERCENTRRANK\" function is adapted again to finally compare the relative risk in a group. Table 3 shows the variable lists adapted when evaluating fire risk. To keep the independence of data, data was separated into model construction and model verification. The fire data during 2011-2017 was used to find out the optimized weights of each factor and then they were verified using the fire data during 2018-2019 data. Table 4 shows the weight change for likelihood index of modules. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Module** & **Components** & \begin{tabular}{c} **KFRI** \\ **Weights** \\ \end{tabular} & \begin{tabular}{c} **NFKRI** \\ **Weights** \\ \end{tabular} \\ \hline \multirow{6}{*}{Basic (B)} & Years (y) & Not used & 6.99 \\ \cline{2-4}  & Floors (e) & 1.25 & 2.42 \\ \cline{2-4}  & \
\begin{tabular}{c} Structure (s) \\ \end{tabular} & 1.25 & 4.70 \\ \cline{2-4}  & Size (g) & 1.43 & 2.31 \\ \cline{2-4}  & Fire load (q) & 2.5 & 2.28 \\ \cline{2-4}  & Fire facility (i1) & 1.82 & 8.62 \\ \cline{2-4}  & Gas facility (i2) & 1.44 & 9.98 \\ \cline{2-4}  & Hazardous material facility (i3) & 1.33 & 9.98 \\ \cline{2-4}  & Electricity facility (i4) & 1.41 & 9.98 \\ \hline \multirow{6}{*}{Process (P)} & Basic process (p0) & 1.15 & 9.93 \\ \cline{2-4}  & Hazardous material (p1) & 1.03 & 1.18 \\ \cline{2-4}  & Hot work (p2) & 1.03 & 1.39 \\ \cline{2-4}  &
\begin{tabular}{c} Flammable gas (p3) \\ \end{tabular} & 1.03 & 1.29 \\ \cline{2-4}  & High temperature or pressure (p4) & 1.03 & 1.44 \\ \cline{2-4}  & Static electricity (p5) & 1.03 & 1.51 \\ \cline{2-4}  & Dust (p6) & 1.03 & 1.96 \\ \cline{2-4}  & High voltage (p7) & 1.03 & 9.99 \\ \hline Safety management (M) & Safety management (M) & 4.25 & 1.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Weight change for likelihood index. \begin{table}
\begin{tabular}{c c} \hline \hline
**Application Range** & **Variables** \\ \hline \multirow{6}{*}{All} & Years (y), Floors (e), Structure (s), Size (g), Fire load (q), Fire facility (i1), \\  & Gas facility (i2), Hazardous material facility (i3), Electricity facility (i4), \\  & Safety management(M), Fire extinguisher (F1), Standpipe (F2), Hydrant (F3), \\  & Sprinkler (F4), Total flooding gas system (F5), Fire detection system (A1), \\  & Emergency alarm system(A2), Emergency notifying system (A3), \\  & Fire compartment (V1), Evacuation system (V2), Smoke control system (S1), \\  & Auxiliary equipment required for fire brigade (S2) \\ \hline General buildings only & Multi use (c), Movement of occupants (d), Accommodation (r) \\ \hline \multirow{3}{*}{Factories only} & Basic process (p0), Hazardous material(p1), Hot work (p2), Flammable gas (p3), \\  & High temperature or pressure(p4), Static electricity (p5), Dust (p6), \\  & High voltage (p7) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Application range of variables. The components and modules of likelihood and severity indexes were selected by KFPA experienced surveyors from the fire engineering point of view. In this paper, we tried to optimize the weights of each component for the likelihood index. \[\textit{Likelihood index}=\frac{B\times I\times P}{M} \tag{8}\]  
Inspection data was combined with fire incidents data, and then classified into whether fire happened or not. In order to optimize the weights of each component, the concept of deviation based on the mean value of the likelihood index with or without fire incident was used.