해양모니터링 자료의 장기결측 보충 기법
Long-gap Filling Method for the Coastal Monitoring Data
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참고문헌
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초록
해양모니터링 자료에서 빈번하게 발생하는 장기결측구간의 자료 보충기법을 제안한다. 제안하는 방법은 결측구간의 장기변동 추세 성분과 단기변동 잔차성분을 추정하여 조합하는 방식으로 결측구간의 미지 정보를 추정한다. 이 방법을 이용하여 울릉도 해상부이 자료의 수온 항목, 약 1개월 정도의 장기결측 구간의 자료를 보충하였으며, 부이에서 관측하는 자료 항목에 대해서도 결측 보충을 수행하였다. 보충된 자료는 항목에 따라 차이를 보이지만 변동양상이 적절하게 재현되는 것으로 파악되었다. 이 방법은 추세추정과 잔차 반영에 따른 편향오차와 분산오차가 발생하지만, 장기결측으로 인한 통계적인 측도 추정의 편향오차는 크게 절감하는 것으로 파악되었다. 결측보충 모형의추정 RMS 오차의 평균과 90% 신뢰구간은 각각 0.93, 0.35~1.95 범위이다.
Abstract
Technique for the long-gap filling that occur frequently in ocean monitoring data is developed. The method estimates the unknown values of the long-gap by the summation of the estimated trend and selected residual components of the given missing intervals. The method was used to impute the data of the long-term missing interval of about 1 month, such as temperature and water temperature of the Ulleungdo ocean buoy data. The imputed data showed differences depending on the monitoring parameters, but it was found that the variation pattern was appropriately reproduced. Although this method causes bias and variance errors due to trend and residual components estimation, it was found that the bias error of statistical measure estimation due to long-term missing is greatly reduced. The mean, and the 90% confidence intervals of the gap-filling model’s RMS errors are 0.93 and 0.35~1.95, respectively.
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