Predictive Analysis of Fire Risk Factors in Gyeonggi-do Using Machine Learning
- Seo, M.S.; Castillo, Osorio E.E.; Yoo, H.H.
- Issue Date
- Korean Society of Surveying
- Gradient Boosted Regression Tree; Machine Learning; Multiple Regression Analysis; Prediction of Fire Risk Factors; Random Forest; Support Vector Machine
- Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, v.39, no.6, pp.351 - 361
- Journal Title
- Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
- Start Page
- End Page
- 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 Gyeonggido 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. ? 2021 Korean Society of Surveying. All rights reserved.
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