# Fire Risk Assessment Models Using Statistical Machine Learning and Optimized Risk Indexing
## 6 Conclusions  
In this paper, we proposed a model for fire risk assessment using statistical machine learning and optimized risk indexing. In general, to predict fire risk prediction is very difficult, this is because fires occur very rarely and the causes that affect fire occurrence are very diverse. So, we need more advanced models for fire risk prediction and assessment. Currently, the KFRI is widely used in practice to evaluate fire risk and then decide the discount rate of fire insurance premium in Korea. In this paper, we compared our proposed models with the KFRI and previous other researches. For the fire risk forecasting and management, we considered and constructed three models that are logistic regression, DNN and NKFRI (new fire risk index). The logistic regression analysis and DNN learning algorithm are based on the statistical machine learning. Also, the NKFRI is an optimized risk indexing model, an extended model of KFRI. In the experimental results, we found that the DNN and NKFRI provide more improved performance than traditional KFRI or previous research. However, it is difficult to actually apply machine learning to sensitive areas such as insurance premium decisions despite its better performance, because it is hard to explain the result clearly.  
Therefore, in our future works, we will try to solve this problem preferentially. To increase the explanatory power of the model, we will apply probability theory and statistical inference to fire prediction models based on machine learning and fire risk indexing. Because of the characteristic of fire occurrence, it is very difficult to accurately predict the fire occurrence and risk. Although the accuracy of fire prediction by our proposed model has been improved compared to the existing research results, the accuracy of our fire prediction was not raised to the highest level. So, additional research strategies are needed to develop a model that can improve the performance of fire prediction. In this paper, we considered DNN with large hidden layers and nodes and got the prediction accuracy of 0.7514. We will consider convolutional neural networks with convolution operation for improving fire prediction accuracy larger than 0.7514. One of the advantages of using our models is it is easy to combine with other information. There are plenty of chances of more sources or information related to fire is open to public in this big data era. This process makes it easy to find out the optimal weights, whereas the AHP process requires a quantity of time and collaboration among engineers to get proper weights of each component. In addition, we will consider more advanced models such as Bayesian deep learning for improved fire risk prediction.  
This paper contributes to the real domains related to fire risk assessment such as calculation and imposition of fire insurance premiums. Traditionally, the calculation of fire hazard ratings for buildings and factories relies on the subjective knowledge of a group of fire experts, but the results of our study have enabled this work to be quantifiable and objective. In our research, we focused on the fire risk forecasting and assessment for buildings and factories. We also expect that the application of this paper can be extended to fire risk related to forests and other domains.  
[PERSON] designed this research and collected the data set for the experiment. Also, [PERSON] developed proposed methodology. [PERSON] wrote this manuscript and made original draft. [PERSON] and [PERSON] analyzed the data to show the validity of this paper and performed all the research steps. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This research was supported by a grant(2018-MOIS31-009) from Fundamental Technology Development Program for Extreme Disaster Response funded by Korean Ministry of Interior and Safety (MOIS). The authors declare no conflict of interest.