[논문]머신러닝을 이용한 경기도 화재위험요인 예측분석

서민송; 에베르 엔리케 카스티요 오소리오; 유환희

doi:10.7848/ksgpc.2021.39.6.351

머신러닝을 이용한 경기도 화재위험요인 예측분석
Predictive Analysis of Fire Risk Factors in Gyeonggi-do Using Machine Learning 원문보기

한국측량학회지 = Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, v.39 no.6, 2021년, pp.351 - 361

서민송 (Dept. of Urban Engineering, Gyeongsang National University) , 에베르 엔리케 카스티요 오소리오 (Dept. of Urban Engineering, Gyeongsang National University) , 유환희 (ERI, Dept. of Urban Engineering, Gyeongsang National University)

초록
AI-Helper

화재는 막대한 재산과 인명피해를 초래하고 있으며 크고 작은 화재가 지속해서 발생하고 있다. 따라서 본 연구는 화재 유형별로 화재에 영향을 미치는 각종 위험요인을 예측하고자 한다. 전국에서 화재 발생 건수가 가장 많은 경기도를 대상으로 화재발생위험요인 예측분석을 실시하였다. 또한, 머신러닝 방법인 SVM, RF, GBRT를 활용하여 각 모형의 정확성을 MAE,RMSE를 통해 적합도가 높은 모형을 제시하였으며 이를 토대로 경기도 화재발생요인 예측분석을 실시하였다. 머신러닝 방법 3가지를 비교분석한 결과 RF가 MAE 1.517, RMSE 1.820으로 나타났으며 MAE, RMSE 검증데이터 및 시험데이터의 경우 MAE값 0.024, RMSE값 0.12의 차이로 매우 유사하게 나타나 가장 우수한 예측력으로 나타났다. RF기법을 적용하여 분석한 결과 공통적으로 발화장소가 화재발생에 가장 큰 영향을 주는 위험요인으로 나타났다. 이러한 연구 결과는 화재발생에 영향을 주는 요인들의 위험순서를 파악하여 화재안전관리의 유용한 자료로 활용될 것으로 예상된다.

Abstract ▼ AI-Helper

The seriousness of fire is rising because fire causes enormous damage to property and human life. Therefore, this study aims to predict various risk factors affecting fire by fire type. The predictive analysis of fire factors was carried out targeting Gyeonggi-do, which has the highest number of fires in the country. For the analysis, using machine learning methods SVM (Support Vector Machine), RF (Random Forest), GBRT (Gradient Boosted Regression Tree) the accuracy of each model was presented with a high fit model through MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error), and based on this, predictive analysis of fire factors in Gyeonggi-do was conducted. In addition, using machine learning methods such as SVM (Support Vector Machine), RF (Random Forest), and GBRT (Gradient Boosted Regression Tree), the accuracy of each model was presented with a high-fit model through MAE and RMSE. Predictive analysis of occurrence factors was achieved. Based on this, as a result of comparative analysis of three machine learning methods, the RF method showed a MAE = 1.765 and RMSE = 1.876, as well as the MAE and RMSE verification and test data were very similar with a difference between MAE = 0.046 and RMSE = 0.04 showing the best predictive results. The results of this study are expected to be used as useful data for fire safety management allowing decision makers to identify the sequence of dangers related to the factors affecting the occurrence of fire.

주제어

표/그림 (12)

그림 Fig. 1. Research flowchart
표 Table 1. Fire occurrence factor variables
그림 Fig. 2. Number of fires by metropolitan city and province nationwide(2017~2019)
표 Table 2. Analysis of multiple regression
그림 Fig. 3. Number of cases by fire occurrence factors in Gyeonggi-do
표 Table 3. Analysis of SVM
표 Table 4. Analysis of RF
표 Table 5. Analysis of GBRT
표 Table 6. Comparative analysis of predictive power by model in Gyeonggi-do
표 Table 7. Results of RF analysis by factors
표 Table 8. Evaluation of importance by RF-oriented factors
표 Table 9. Evaluation of importance by RF-oriented factors

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