In meteorological data, various studies are being conducted to improve the prediction performance of rainfall with irregular patterns, unlike temperature and solar radiation with certain patterns. Especially in the case of the short-term forecast model for Dong-Nae Forecasts provided by the Korea Meteorological Administration (KMA), forecast data are provided at 6-hour intervals, and there is a limit to analyzing the impact of disasters. In this study, Hydrological Quantitative Precipitation Forecast (HQPF) information was generated by applying the machine learning method to Local ENsemble prediction system (LENS), Radar-AWS Rainrates (RAR), AWS and ASOS observation data and Dong-Nae Forecast provided by the KMA. Through the preprocessing process, the temporal and spatial resolutions of all the data were converted to the same resolution, and the predictor of machine learning was derived through the factor analysis of the predictor. Considering the processing speed and expandability, the XGBoost method of machine learning was applied, and the Probability Matching (PM) method was applied to improve the prediction accuracy of heavy rainfall. As a result of evaluating the HQPF performance produced for 14 heavy rainfall events that occurred in 2020, it was found that the predicted performance of HQPF was improved quantitatively and qualitatively.

Abstract
1. 서론
2. 재료 및 방법
    1) 기계학습 인자
    2) HQPF 생산시스템
    3) 집중호우 사상 선정
    4) 예측성능 검증 방법
3. 분석 결과
    1) 전체 호우사상 분석 결과
    2) 관측지점별 호우사상 분석 결과
4. 요약 및 결론
References

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• 정영윤(2(주)에코브레인 신사업개발팀) | Yeong Yun Jeong (New Bussiness Development Team, ECOBRAIN Co. Ltd.)
• 지용근((주)에코브레인 환경사업팀) | Yong-Keun Ji (Environment Business Team, ECOBRAIN Co. Ltd.)
• 이영미((주)에코브레인) | Young-Mi Lee (ECOBRAIN Co. Ltd.)
• 김병식(강원대학교 방재전문대학원 도시환경&재난관리전공) | Byung-Sik Kim (Department of Urban & Environmental Disaster Prevention Engineering School of Disaster Prevention) Correspondence