# Fire Risk Assessment Models Using Statistical Machine Learning and Optimized Risk Indexing
## 4 Experimental Results
In this paper, we carried out the logistic regression analysis and DNN using the 18 explanatory variables and one response variable. We divided the entire data into training and test data sets. We extracted 70% randomly from our data for training and 30% for test. After various trials and errors, we finally designed our DNN architecture as follows: hidden layers, 3; hidden nodes in each hidden layer, 100; activation function, sigmoid; learning rate, 0.8; learning momentum, 0.5; epoch, 100; and weight decay, 0.000001. Also, we used R data language and its packages for fire data analysis [19]. Using the training data set, we constructed the forecasting models. To evaluate the performance of structured models, we used the test data set. Table 2 shows the comparison result of prediction accuracy between the models. KFRI is the predictive model currently used, and the accuracy result of the KFRI model is to predict whether a fire has occurred using the KFRI. Therefore, we found that fire prediction is a very difficult task. The prediction accuracy of logistic regression was higher than that of KFRI, but the performance improvement was not significant. Finally, we can see that the accuracy of fire prediction of the DNN model is significantly higher than that of KFRI or logistic regression. For further performance improvement, we considered fire risk indexing. The fire risk indexing has been recommended and used as a rapid assessment to evaluate the fire risk of alternative concepts for large buildings. KFPA also uses this fire risk indexing method known as one of the KFRI models. In Table 1, we show the components (variables) for fire risk indexing and calculation formula of KFRI for manufacturing facilities. The hazard components of KFRI are placed in the numerator and the countermeasure components are placed in the denominator to reflect the risk of building. We calculate the KFRI by equation (6):  
\[KFRI=\frac{B\times I\times P}{M\times F\times A\times V\times S\times G}\times 100 \tag{6}\]  
where  
B = Basic hazards or intrinsic hazards such as number of floor, structure, size, fire load, etc. I = Ignition hazards due to fire, gas, electrical facilities, and hazardous material  
P = Process hazards apply only for factories consist of basic process, hazardous material treating process, hot work process, etc. M = Building safety management based on fire drill, education, hot work, smoking control, etc. F = Fire protection equipment and system such as fire extinguisher, fire sprinkler, standpipe, etc. A = Fire alarm system such as fire detecting system, notifying system, etc. V = Fire compartment and evacuation system  
S = Smoke control system and auxiliary equipment required for fire brigade. G = Public fire service  
The higher the risk, the higher the value of KFRI. Each component of KFRI is calculated combining its weight and inspection results by KFPA surveyors. The weights of each component for KFRI are originally decided by the analytical hierarchy process (AHP) with experienced KFPA field surveyors. After collecting enough inspection and fire incidents data for many years, we tried various approaches to improve its performance. New KFRI (NKFRI) was considered in order to compare the risks among factories with a total area of more than 3,000 m\({}^{2}\) and other specific buildings designated by the Korean law [20]. In the NKFRI model, we performed the fire risk indexing for factories. NKFRI more specifically introduced the concepts of likelihood and severity of fire by rearranging variables and then assigning optimal weight for each components based on its deviation. One of the main reasons to develop NKFRI is to make it easier to compare the risk among factories that have similar processes. In order to evaluate the relative standing of a value within a group, \"PERCENTRRAN\" function of Excel 2016 was used. \[NKFRI=\textit{Percentrank of likelihood index}\times\textit{Percentrank of severity index} \tag{7}\]