강우·소방·사회취약성 데이터를 활용한 복합재난 위험도 예측 및 소방자원 배치 전략: 서초구 사례의 MRA-Random Forest 비교분석

초록

This study aims (Purpose) to predict the risk of compound disasters in Seocho District, Seoul, by integrating rainfall, firefighting activity, and social vulnerability data, and to propose strategies for the optimal allocation of firefighting resources.The research employs (Method) hourly rainfall data from 2020 to 2024, detailed fire dispatch records, 119 rescue and emergency activity logs, and demographic and social vulnerability indicators extracted from statistical yearbooks, and calculates dispatch delays, arrival times, and total response times using reported, dispatch, and arrival timestamps. Social vulnerability variables include the proportion of residents aged 65 or older, the proportion of registered persons with disabilities, the share of single-person households, and the percentage of semi-basement or basement dwellings.Multiple Regression Analysis and Random Forest models are applied to predict hourly fire dispatch counts and fire occurrence, and their predictive performances are compared using 2024 data as a validation set; results (Result) show that the Random Forest model achieves a lower mean absolute error than the regression model in count prediction and a higher AUC in occurrence classification.Variable importance analysis further indicates that cumulative rainfall over the previous 24 hours, current rainfall, time of report (day/night), and the proportions of elderly residents, registered disabled residents, semi-basement or basement dwellings, and single-person households are major contributors to predicted compound disaster risk.Overall, the study concludes (Conclusion) that integrating meteorological, firefighting, and socio-demographic data enables more accurate compound disaster risk prediction and supports socially responsive resource allocation strategies, thereby contributing to data-driven decision-making in urban disaster management.

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