한국수자원학회논문집 (Journal of Korea Water Resources Association)

  • 제47권11호
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  • Pages.1095-1105
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  • 2014
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  • 2799-8746(pISSN)
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  • 2799-8754(eISSN)
한국수자원학회 (Korea Water Resources Association)
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SVM 회귀 모형을 활용한 격자 강우량 상세화 기법

Spatial Downscaling of Grid Precipitation Using Support Vector Machine Regression

  • 문희원 (성균관대학교 건설환경시스템공학과) ;
  • 백종진 (성균관대학교 건설환경시스템공학과) ;
  • 황석환 (한국건설기술연구원 수자원연구실) ;
  • 최민하 (성균관대학교 수자원대학원 수자원학과)
  • Moon, Heewon (Dept. of Civil, Architectural and Environmental System Engineering, Sungkyunkwan Univ.) ;
  • Baik, Jongjin (Dept. of Civil, Architectural and Environmental System Engineering, Sungkyunkwan Univ.) ;
  • Hwang, Sukhwan (Water Resources Research Division, Korea Institute of Civil Engineering and Building Technology) ;
  • Choi, Minha (Dept. of Water Resources, Graduate School of Water Resources, Sungkyunkwan Univ.)
  • 투고 : 2014.09.05
  • 심사 : 2014.11.03
  • 발행 : 2014.11.30
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초록

본 연구에서는 Tropical Rainfall Measuring Mission (TRMM) 3B43 V7 (25 km)의 월 누적 격자 강우량을 1 km 해상도로 상세화하기 위해 Support Vector Machine (SVM) 회귀를 활용한 상세화 기법을 제안하였다. 비선형 예측모델인 SVM은 상세화의 기반이 되는 다양한 수문기상인자와 강우 발생간의 월별 상관성 구축에 효율적으로 활용되었다. 상세화된 격자 강우는 전국에 고루 분포한 64개 지점 관측 강우와의 비교 분석을 통해 상세화 이전의 격자 강우 보다 다소 개선된 정확도를 지니는 것으로 확인되었다. 특히, 상세화 이전 격자 강우가 지니는 양의 Bias가 효과적으로 개선되었다. 상세화 전후의 공간분포 비교에서 두 분포는 평균적으로 유사했으나, 상세화 이전 강우의 공간분포에서 나타나지 않았던 강우의 국지적 특성이 상세화된 공간분포를 통해 잘 표현되는 것을 확인할 수 있었다. 특히, 일부 지점의 과소 및 과대산정이 상세화를 통해 개선되어 전반적인 정확도 향상에 기여하였음을 확인했다. 본 연구에서 제안된 상세화 기법이 적용된 격자 강우는 모델의 정확도 향상을 위한 고해상도 입력자료로 활용될 수 있으며, 추후 연구에서는 SVM 외에 다른 회귀 방식을 활용하여 최적의 강우 상세화 기법 개발에 기여할 수 있을 것으로 보인다.

A spatial downscaling method using the Support Vector Machine (SVM) Regression for 25 km Tropical Rainfall Measuring Mission (TRMM) Monthly precipitation is proposed. The nonlinear relationship among hydrometeorological variables and precipitation was effectively depicted by the SVM for predicting downscaled grid precipitation. The accuracy of spatially downscaled precipitation was estimated by comparing with rain gauge data from sixty-four stations and found to be improved than the original TRMM data in overall. Especially the positive bias of the original TRMM data was effectively removed after the downscaling procedure. The spatial distributions of 25 km and 1 km grid precipitation were generally similar, while the local spatial trend was better detected by 1 km grid precipitation. The downscaled grid data derived from the proposed method can be applied in hydrological modelling for higher accuracy and further be studied for developing optimized downscaling method incorporation other regression methods.

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