국가농림기상센터 - 머신러닝 기법의 산림 총일차생산성 예측 모델 비교.

2019년도

Title	머신러닝 기법의 산림 총일차생산성 예측 모델 비교.
AuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthors	이보라, 장근창, 김은숙, 강민석, 천정화, 임종환
PublicationPublicationPublicationPublicationPublication	201820182018201820182018201820192019201920192019
JournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournalJournal	Atmosphere 9The Journal of Engineering Geology 28Korean Journal of Agricultural and Forest MeteorologyKorean Journal of Agricultural and Forest MeteorologyKorean Journal of Agricultural and Forest Meteorology Korean Journal of Agricultural and Forest Meteorology Korean Journal of Agricultural and Forest Meteorology
비고비고비교	KCI

다음글: 농림생태계 지속가능성 제고를 위한 농림기상학의 도전과 과제; / 관리자; TitleTitle 농림생태계 지속가능성 제고를 위한 농림기상학의 도전과 과제 AuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthorsAuthors 김..

이전글: Improving Usage of the Korea Meteorological Administration's Digital Forecasts in Agriculture: Correction Method for Daytime Hourly Air Temperature over Complex Terrain; / 관리자; TitleTitleTitleTitleTitleTitleTitleTitleTitleTitleTitleTitleTitleTitleTitleTitleTitleTitleTitleTitleTitleTitleTitleTitleTitleTitleTitle 중규모 기상모델(WRF-ARW)을 활용한 서울시 옥상노화와 쿨루프의 기온저감 효과 분석Development of a micro scale CFD model to predict wind environment on..