| Title |
Machine Learning?Based Analysis of Construction Site Fire Risk Factors Considering Weather Conditions |
| Authors |
김원창(Kim, Won-Chang) ; 최형길(Choi, Hyeong-gil) ; 이태규(Lee, Tae Gyu) |
| DOI |
https://doi.org/10.5659/JAIK.2026.42.8.365 |
| Keywords |
Construction Site Fire; Machine Learning; Weather; Prediction Model |
| Abstract |
In this study, a machine learning-based multi-classification model was developed and evaluated to classify the risk of construction site fires
using weather conditions. A database was created using the number of construction site fires provided by the Fire and Rescue Service and
weather condition data provided by the Korea Meteorological Administration, and quartiles were used to set the criteria for construction site
fire risk. Seven machine learning-based multi-classification algorithms were then used for modeling and evaluation. The cumulative weekly
construction site fires were negatively correlated with weather conditions related to temperature (-0.84 to -0.75), negatively correlated with
humidity (-0.83 to -0.793), and positively correlated with barometric pressure (0.78). We classified the construction site fire risk criteria into
three levels (fire0, fire1, and fire2), and the XGBoost and LightGBM models showed the best performance in the modeling results. The
statistical test of weather conditions by construction site fire risk showed that the higher the fire risk, the more winter and spring weather
conditions, and there was a statistically significant difference between the groups at the 0.01 significance level. |