Analysis of Fire Risk Factors in Seoul, Korea, by Machine Learning

Abstract

Different types of fire accidents in the urban area of Seoul, Korea are continuously occurring, causing risk and damage to property and life. In this study, we analyze various spatial and non-spatial fire risk factors by applying machine learning techniques to predict their level of importance in future events. We use the data on fire accident for three years (2017-2019) published by the Korean Fire Service and the Seoul Metropolitan Government. Regarding the machine learning techniques, we use support vector machine (SVM), random forest (RF), and gradient boosted regression tree (GBRT). As the first phase, a multiple regression analysis is performed to select seven main factors related to fire occurrence. In the second phase, we calculate the mean absolute error (MAE) and root mean squared error (RMSE) using validation and test data for the machine learning techniques, revealing that RF obtains ideal results. In the third phase, we analyze the importance of the seven fire factors using RF, resulting in the ignition condition (produced by electrical, mechanical, and chemical reasons) being the main factor in fire occurrence. This study is expected to be used as an important guideline to define urban fire reduction and management measures in Seoul, the capital of South Korea.