관측자료 기반 격자 생성자료 생성 및 예측모형 연구

• 영문명: Study for Grid Interpolation Data Generation and Prediction Models based on Observed Data
• 발행기관: 전남대학교 수산과학연구소
• 저자명: 조창제(Chang Je Cho) 곽경일(Gyeong Il Kwak) 주현석(Hyun Seok Joo) 김국진(Kuk Jin Kim)
• 간행물 정보: 『수산과학연구소 논문집』제30권 제1호, 25~31쪽, 전체 7쪽
• 주제분류: 농수해양 > 수산학
• 파일형태: PDF
• 발행일자: 2021.02.28

영문 초록

Natural disasters are increasing due to recent climate change. Rainfall directly or indirectly affects aquaculture and agriculture. Therefore, it is important to anticipate and prepare for this. In this study, a study on the performance comparison between spatial interpolation techniques was conducted based on observed data, and predicted based on the data produced by the spatial interpolation techniques. The spatial interpolation methods compared the inverse distance weighting method, kriging and spatial random forest through MAE (Mean absolute error), RMSE (Root mean square error), correlation coefficient. The verification of predicted data was compared through MAE, CSI (Critical Success Index), FAR (False Alarm Ratio), POD (Probability of Detection) and ETS (Equitable Threat Score).

목차

서론

연구 자료

공간보간기법 성능 비교 연구

공간자료 기반 예측모형 생성 및 평가

결론

요약

키워드

Grid data Interpolation Machine learning Precipitation Spatial random forest