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Development of a Model Combining Covariance Matrices Derived from Spatial and Temporal Data to Estimate Missing Rainfall Data

공간 데이터와 시계열 데이터로부터 유도된 공분산행렬을 결합한 강수량 결측값 추정 모형

Sung, Chan Yong
성찬용

  • Received : 2012.10.27
  • Accepted : 2013.02.12
  • Published : 2013.03.29

Abstract

This paper proposed a new method for estimating missing values in time series rainfall data. The proposed method integrated the two most widely used estimation methods, general linear model(GLM) and ordinary kriging(OK), by taking a weighted average of covariance matrices derived from each of the two methods. The proposed method was cross-validated using daily rainfall data at thirteen rain gauges in the Hyeong-san River basin. The goodness-of-fit of the proposed method was higher than those of GLM and OK, which can be attributed to the weighting algorithm that was designed to minimize errors caused by violations of assumptions of the two existing methods. This result suggests that the proposed method is more accurate in missing values in time series rainfall data, especially in a region where the assumptions of existing methods are not met, i.e., rainfall varies by season and topography is heterogeneous.

Keywords

Missing rainfall data;Geostatistics;General linear model;Ordinary kriging

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