[논문]머신러닝 기법을 이용한 재해강도 분류모형 개발

이승민; 백선욱; 이준학; 김경탁; 김수전; 김형수

doi:10.3741/jkwra.2023.56.4.261

머신러닝 기법을 이용한 재해강도 분류모형 개발
Development of disaster severity classification model using machine learning technique 원문보기

Journal of Korea Water Resources Association = 한국수자원학회논문집, v.56 no.4, 2023년, pp.261 - 272

이승민 (인하대학교 스마트시티공학과) , 백선욱 (인하대학교 스마트시티공학과) , 이준학 (인하대학교 스마트시티공학과) , 김경탁 (한국건설기술연구원 수자원하천연구본부) , 김수전 (인하대학교 사회인프라공학과) , 김형수 (인하대학교 사회인프라공학과)

초록
AI-Helper

최근 급격한 도시화와 기후변화에 따라 재난에 의한 피해가 증가하고 있다. 국내 기상청에서는 표준 경보(주의보, 경보)를 전국적으로 통일된 표준 경보 기준(3시간 및 12시간 최대 누적강우량)에 따라 발령하여 재해에 따른 지역별, 재난 사상별 특성이 고려되지 않은 문제점이 있다. 따라서 본 연구에서는 서울특별시, 인천광역시, 경기도의 호우·태풍에 대한 재해 피해액 및 누적강우량을 활용하여 대상지역별 재해강도에 따른 단계별 기준을 설정하고, 강우에 따라 발생할 수 있는 재해의 강도를 분류하는 모형을 개발하고자 하였다. 즉, 본 연구에서는 호우·태풍에 의한 재해 피해액 누적 분포 함수의 분위별로 재해강도의 범주(관심, 주의, 경계, 심각 단계)를 분류하였고, 재해강도의 범주에 따른 누적강우량 기준을 대상 지자체별로 제시하였다. 그리고 지자체별 재해강도 분류모형 개발을 위해 4가지(의사결정나무, 서포트 벡터 머신, 랜덤 포레스트, XGBoost)의 머신러닝 모형을 활용하였는데 강우량, 누적강우량, 지속시간 최대 강우량(3시간, 12시간), 선행강우량을 독립변수로 이용하여 종속변수인 지자체별 재해강도를 분류하였다. 각 모형별 F1 점수를 이용한 정확도 평가 결과, 의사결정나무의 F1 점수가 0.56으로 가장 우수한 정확도를 보였다. 본 연구에서 제시한 머신러닝 기반 재해강도 분류모형을 활용하면 호우·태풍에 의한 재해에 대한 지자체별 위험 상태를 단계별로 파악할 수 있어, 재난 담당자들의 신속한 의사결정을 위한 기초 자료로 활용될 수 있을 것으로 판단된다.

Abstract ▼ AI-Helper

In recent years, natural disasters such as heavy rainfall and typhoons have occurred more frequently, and their severity has increased due to climate change. The Korea Meteorological Administration (KMA) currently uses the same criteria for all regions in Korea for watch and warning based on the maximum cumulative rainfall with durations of 3-hour and 12-hour to reduce damage. However, KMA's criteria do not consider the regional characteristics of damages caused by heavy rainfall and typhoon events. In this regard, it is necessary to develop new criteria considering regional characteristics of damage and cumulative rainfalls in durations, establishing four stages: blue, yellow, orange, and red. A classification model, called DSCM (Disaster Severity Classification Model), for the four-stage disaster severity was developed using four machine learning models (Decision Tree, Support Vector Machine, Random Forest, and XGBoost). This study applied DSCM to local governments of Seoul, Incheon, and Gyeonggi Province province. To develop DSCM, we used data on rainfall, cumulative rainfall, maximum rainfalls for durations of 3-hour and 12-hour, and antecedent rainfall as independent variables, and a 4-class damage scale for heavy rain damage and typhoon damage for each local government as dependent variables. As a result, the Decision Tree model had the highest accuracy with an F1-Score of 0.56. We believe that this developed DSCM can help identify disaster risk at each stage and contribute to reducing damage through efficient disaster management for local governments based on specific events.

주제어

표/그림 (17)

그림 Fig. 1. Location of study area and weather observation station
표 Table 1. Basic statistics of variables
그림 Fig. 2. Conceptual diagram of decision tree model
그림 Fig. 3. Conceptual diagram of support vector machine model
그림 Fig. 4. Conceptual diagram of random forest model
그림 Fig. 5. Conceptual diagram of XGBoost model (Kim et al., 2019)
표 Table 2. Confusion matrix for performance evaluation of the model (Karimi, 2021)
그림 Fig. 6. Cumulative distributions of damage and rainfall for each local government
표 Table 3. Basic statistics of independent variables for Gyeonggi province
그림 Fig. 7. Conceptual diagram of SMOTE method (Alencar, 2018)
표 Table 4. Classification of cumulative rainfall criteria for each local government
표 Table 5. Comparision of standard criteria and disaster risk stages from cumulative distributions
표 Table 6. Comparison of F1-Score for SMOTE Application in the Learning Process of each Model
표 Table 7. Confusion matrix of performance evaluation for the learning process of each model
표 Table 8. Performance comparison for the learning process of models in each local government
표 Table 9. Confusion matrix of performance evaluation for the evaluation process of each model
표 Table 10. Performance comparison for the evaluation process of models in each local government

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