For the purposes of enhancing usability of Numerical Weather Prediction (NWP), the quantitative precipitation prediction scheme by machine learning has been proposed. In this study, heavy rainfall was corrected for by utilizing rainfall predictors from LENS and Radar from 2017 to 2018, as well as machine learning tools LightGBM and XGBoost. The results were analyzed using Mean Absolute Error (MAE), Normalized Peak Error (NPE), and Peak Timing Error (PTE) for rainfall corrected through machine learning. Machine learning results (i.e. using LightGBM and XGBoost) showed improvements in the overall correction of rainfall and maximum rainfall compared to LENS. For example, the MAE of case 5 was found to be 24.252 using LENS, 11.564 using LightGBM, and 11.693 using XGBoost, showing excellent error improvement in machine learning results. This rainfall correction technique can provide hydrologically meaningful rainfall information such as predictions of flooding. Future research on the interpretation of various hydrologic processes using machine learning is necessary.

Abstract
 1. 서론
 2. 자료 및 연구방법
  2.1. 수치 자료
  2.2. 알고리즘 개발 방법
  2.3. 호우사례 선정
 3. 결과 및 고찰
  3.1. 통계 검증
  3.2. 호우사례별 공간분포 비교
 4. 결 론
 REFERENCES

• 이영미((주)에코브레인) | Young-Mi Lee (ECOBRAIN Co. Ltd.)
• 고철민((주)에코브레인) | Chul-Min Ko (ECOBRAIN Co. Ltd.) Corresponding author
• 신성철((주)에코브레인) | Seong-Cheol Shin (ECOBRAIN Co. Ltd.)
• 김병식(강원대학교 도시·환경방재공학전공) | Byung-Sik Kim (Department of Urban & Environmental Disaster Prevention Engineering, Kangwon National University)