Complex Disaster Risk Prediction and Fire Resource Allocation Using Rainfall, Fire Service, and Social Vulnerability Data: An MRA–Random Forest Comparison in Seocho-gu, Seoul

doi:10.15683/kosdi.2025.12.31.1169

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2025 Vol.21, Issue 4 Preview Page Next Page

Research Article

Complex Disaster Risk Prediction and Fire Resource Allocation Using Rainfall, Fire Service, and Social Vulnerability Data: An MRA–Random Forest Comparison in Seocho-gu, Seoul 강우·소방·사회취약성 데이터를 활용한 복합재난 위험도 예측 및 소방 자원 배치 전략: 서초구 사례의 MRA–Random Forest 비교 분석: 심응수¹ㆍ박인선²*
Eung Soo Shim¹, In-seon Park²*; ¹Graduate Student, Department of ICT Convergence Safety, The Graduate School of Chung-Ang University, Seoul, Republic of Korea
²Professor, Department of ICT Convergence Safety, The Graduate School of Chung-Ang University, Seoul, Republic of Korea

31 December 2025. pp. 1169-1178,

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Abstract

Purpose: This study aims 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. Method: The research employs 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. Result: 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 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. Conclusion: Overall, the study concludes 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.

Keywords

Compound Disaster

Random Forest

Social Vulnerability

Fire Dispatch

Urban Disaster Management

연구목적: 본 연구는 서울특별시 서초구를 대상으로 강우, 소방 활동, 사회 취약성 데이터를 통합하여 복합재난 위험도를 예측하고, 이를 기반으로 소방 자원 배치 전략을 제시하는 것을 목적으로 한다. 연구자료는 2020–2024년 서초구 시간별 강우량 자료, 화재 출동 개별 내역, 119 구조·구급활동 실적, 통계 연보에서 추출한 인구·주거·사회 취약성 지표를 사용하였다. 특히 신고일시·출동일시·도착일시를 활용하여 출동 지연, 도착시간, 총 대응 시간을 산출하고, 65세 이상 인구 비율, 장애인 등록 인구 비율, 1인 가구 비율, 반지하·지하 주택 비율을 사회 취약성 변수로 구성하였다. 연구방법: 분석 방법으로는 먼저 화재 출동 내역을 이용해 연도·월·시간대별 출동 건수와 평균 도착시간, 평균 총 대응 시간을 산출하여 기초 통계를 제시하고, 강우량 및 사회 취약성 지표와 결합하여 복합재난 발생에 영향을 미치는 설명변수를 구성하였다. 이후 다중회귀분석(Multiple Regression Analysis, MRA)과 Random Forest(RF)를 이용하여 시간 단위 화재 출동 건수 및 발생 여부를 예측하고, 두 모형의 성능을 비교하였다. 2024년을 검증자료로 사용한 결과, 시간당 화재 출동 건수 예측에서 MRA 모형의 평균절대오차(MAE)는 0.24건, RF 모형의 MAE는 0.18건으로, RF가 약 25.0% 낮은 예측 오차를 보였다. 화재 발생 여부(해당 시간에 1건 이상 발생)의 분류 성능을 AUC로 비교한 결과, 로지스틱 회귀는 0.83, RF 분류기는 0.90을 기록하여 RF가 우수한 성능을 나타냈다. 연구결과: RF 변수 중요도 분석 결과, 이전 24시간 누적 강우량, 현 시각 강우량, 신고 시간의 시간대(야간 여부), 65세 이상 인구 비율, 장애인 등록 인구 비율, 반지하·지하 주택 비율, 1인 가구 비율순으로 중요도가 높게 나타났다. 이는 강우 특성뿐 아니라 고령자 및 장애인 밀집 지역, 주거 취약 지역, 1인 가구 밀집 지역이 복합재난 위험에 유의미한 영향을 미침을 시사한다. 마지막으로, 예측된 위험도와 사회 취약성 지수를 결합하여 행정동별 우선 대응 순위를 제시하고, 이를 기반으로 소방 자원 배치 시나리오를 제안하였다. 결론: 본 연구는 기상·소방·사회통계 데이터를 결합한 복합재난 위험도 예측과 사회 취약성을 고려한 자원 배치 전략을 함께 제시함으로써, 도시형 재난관리에 대한 데이터 기반 의사결정 지원에 기여할 수 있을 것으로 기대된다.

키워드

복합재난

소방 자원 배치

다중회귀분석

Random Forest

사회 취약성

서초구

References

Ahn, J.H., Lee, S.K. (2022). “Integrating social vulnerability into urban flood risk assessment using geospatial analysis.” International Journal of Disaster Risk Reduction, Vol. 78, No. 3, pp. 102145-102155.
Breiman, L. (2001). “Random forests.” Machine Learning, Vol. 45, No. 1, pp. 5-32. 10.1023/A:1010933404324
Choi, K.W. (2021). Optimization of Emergency Medical Service Deployment using Time-Series Forecasting and Simulation. Ph.D. Dissertation, Seoul National University.
Cutter, S.L., Boruff, B.J., Shirley, W.L. (2003). “Social vulnerability to environmental hazards.” Social Science Quarterly, Vol. 84, No. 2, pp. 242-261. 10.1111/1540-6237.8402002
Gook, D.S. (2020). “Predicting fire incidence in metropolitan areas using random forest and meteorological data.” Journal of Fire Safety Engineering, Vol. 35, No. 2, pp. 45-58.
Hwang, Y.M. (2018). Urban Resilience and Climate Change Adaptation. Academic Press, New York.
Jeong, M.C. (2023). A Study on the Impact of Localized Heavy Rainfall on Urban Infrastructure and Emergency Demand. National Disaster Management Research Institute, R-2023-DM-112, Sejong.
Kim, M.S., Lee, J.H. (2021). “A study on urban flood risk prediction using meteorological data.” Journal of the Korean Society for Disaster Information, Vol. 17, No. 3, pp. 45-58.
Korea Meteorological Administration (2024). Meteorological Data Open Portal. https://data.kma.go.kr, (Accessed: Oct. 10, 2024).
Lee, M.H., Park, J.E. (2019). “Compound disaster risk modeling based on machine learning and socioeconomic indicators.” Proceedings of the International Conference on Urban Safety and Disaster Prevention, Tokyo, Japan, pp. 321-340.
Park, J.H. (2022). “Prediction of fire service dispatch demand using machine learning techniques.” Korean Institute of Fire Science and Engineering Journal, Vol. 36, No. 2, pp. 101-112.
Ryu, S.G. (2023). “Comparative analysis of multiple regression and random forest for predicting time-series emergency calls.” Journal of Risk Analysis and Disaster Management, Vol. 15, No. 4, pp. 50-65.
Seocho-gu Office (2023). Seocho-gu Statistical Yearbook 2023. Seocho-gu Office, Seoul.
Song, B.R., Kim, C.H. (2021). “Analyzing the relationship between single-person households and fire vulnerability in high-density urban areas.” Fire Protection Science and Technology, Vol. 25, No. 1, pp. 112-125.

Information

Publisher :The Korean Society of Disaster Information
Publisher(Ko) :한국재난정보학회
Journal Title :Journal of the Society of Disaster Information
Journal Title(Ko) :한국재난정보학회논문집
Volume : 21
No :4
Pages :1169-1178,
DOI :https://doi.org/10.15683/kosdi.2025.12.31.1169

[1] Ahn, J.H., Lee, S.K. (2022). “Integrating social vulnerability into urban flood risk assessment using geospatial analysis.” International Journal of Disaster Risk Reduction, Vol. 78, No. 3, pp. 102145-102155.

[2] Breiman, L. (2001). “Random forests.” Machine Learning, Vol. 45, No. 1, pp. 5-32. 10.1023/A:1010933404324

[3] Choi, K.W. (2021). Optimization of Emergency Medical Service Deployment using Time-Series Forecasting and Simulation. Ph.D. Dissertation, Seoul National University.

[4] Cutter, S.L., Boruff, B.J., Shirley, W.L. (2003). “Social vulnerability to environmental hazards.” Social Science Quarterly, Vol. 84, No. 2, pp. 242-261. 10.1111/1540-6237.8402002

[5] Gook, D.S. (2020). “Predicting fire incidence in metropolitan areas using random forest and meteorological data.” Journal of Fire Safety Engineering, Vol. 35, No. 2, pp. 45-58.

[6] Hwang, Y.M. (2018). Urban Resilience and Climate Change Adaptation. Academic Press, New York.

[7] Jeong, M.C. (2023). A Study on the Impact of Localized Heavy Rainfall on Urban Infrastructure and Emergency Demand. National Disaster Management Research Institute, R-2023-DM-112, Sejong.

[8] Kim, M.S., Lee, J.H. (2021). “A study on urban flood risk prediction using meteorological data.” Journal of the Korean Society for Disaster Information, Vol. 17, No. 3, pp. 45-58.

[9] Korea Meteorological Administration (2024). Meteorological Data Open Portal. https://data.kma.go.kr, (Accessed: Oct. 10, 2024).

[10] Lee, M.H., Park, J.E. (2019). “Compound disaster risk modeling based on machine learning and socioeconomic indicators.” Proceedings of the International Conference on Urban Safety and Disaster Prevention, Tokyo, Japan, pp. 321-340.

[11] Park, J.H. (2022). “Prediction of fire service dispatch demand using machine learning techniques.” Korean Institute of Fire Science and Engineering Journal, Vol. 36, No. 2, pp. 101-112.

[12] Ryu, S.G. (2023). “Comparative analysis of multiple regression and random forest for predicting time-series emergency calls.” Journal of Risk Analysis and Disaster Management, Vol. 15, No. 4, pp. 50-65.

[13] Seocho-gu Office (2023). Seocho-gu Statistical Yearbook 2023. Seocho-gu Office, Seoul.

[14] Song, B.R., Kim, C.H. (2021). “Analyzing the relationship between single-person households and fire vulnerability in high-density urban areas.” Fire Protection Science and Technology, Vol. 25, No. 1, pp. 112-125.

Journal of the Society of Disaster Information ISSN:1976-2208(Print) 2671-5287(Online) 한국재난정보학회논문집

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