Fire Risk Assessment Models Using Statistical Machine Learning and Optimized Risk Indexing

It is very difficult for us to accurately predict occurrence of a fire. But, this is very important to protect human life and property. So, we study fire hazard prediction and evaluation methods to cope with fire risks. In this paper, we propose three models based on statistical machine learning and optimized risk indexing for fire risk assessment. We build logistic regression, deep neural networks (DNN) and fire risk indexing models, and verify performances between proposed and traditional models using real investigated data related to fire occurrence in Korea. In general, fire prediction models currently in use do not provide satisfactory levels of accuracy. The reason for this result is that the factors affecting fire occurrence are very diverse and frequency of fire occurrence is very sparse. To improve accuracy of fire occurrence, we first build logistic regression and DNN models. In addition, we construct a fire risk indexing model for a more improved model of fire prediction. To illustrate comparison results between our research models and current fire prediction model, we use real fire data investigated in Korea between 2011 to 2017. From the experimental results of this paper, we can confirm that accuracy of prediction by the proposed method is superior to the existing fire occurrence prediction model. Therefore, we expect the proposed model to contribute to evaluating the possibility of fire risk in buildings and factories in the field of fire insurance and to calculate the fire insurance premium.