Long-gap Filling Method for the Coastal Monitoring Data

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.

An Empirical Study on the Correction of Missing Data in Coastal Stationary Observation Time Series Data: Focusing on Jumunjin Observatory

“…𝑜 𝑡 = 𝜎(𝑊 𝑜𝑥 𝑥 𝑡 + 𝑊 𝑜𝑚 𝑚 𝑡−1 + 𝑊 𝑜𝑐 𝑐 𝑡 + 𝑏 𝑜 ) (7) 𝑚 𝑡 = 𝑜 𝑡 ⊙ ℎ(𝑐 𝑡 ) (8) 𝑦 𝑡 = ∅(𝑊 𝑦𝑚 𝑚 𝑡 + 𝑏 𝑦 ) (9) 메모리 셀은 여러시간 단계에 걸쳐 정보를 저장할 수 있는 장기 메모리 저장 장치이다(식 6). 입력 게이트에서는 메모리 셀에 얼마나 많은 새로운 정보를 저장해야 하는지 결정한다(식 4).…”