한국농림기상학회지, 제 14권 제4호(2012) (pISSN 1229-5671, eISSN 2288-1859)
Korean Journal of Agricultural and Forest Meteorology, Vol. 14, No. 4, (2012), pp. 269~276
DOI: 10.5532/KJAFM.2012.14.4.269
ⓒ Author(s) 2014. CC Attribution 3.0 License.


작물모형 평가를 위한 통계적 방법들에 대한 비교

김준환, 이충근, 손지영, 최경진, 윤영환
농촌진흥청 국립식량과학원 답작과

(2012년 11월 08일 접수; 2012년 12월 07일 수정; 2012년 12월 10일 수락)

Comparison of Statistic Methods for Evaluating Crop
Model Performance

Junhwan Kim, Chung-Kuen Lee, Jiyoung Shon, Kyung-Jin Choi, Younghwan Yoon
Rice Research Division, National Institute of Crop Science, RDA

(Received November 08, 2012; Revised December 07, 2012; Accepted December 10, 2012)

ABSTRACT
The objective of this short communication is to introduce several evaluation methods to crop model users because the evaluation of crop model performance is an important step to develop or select crop model. In this paper, mean error, mean absolute error, index of agreement, root mean square error, efficiency of model, accuracy factor and bias factor were explained and compared in terms of dimension and observed number. Efficiency of model and index of agreement are dimensionless and independent of number of observation. Relative root mean square, accuracy factor and bias factor are dimensionless and not independent of number of observation. Mean error and mean absolute error are affected by dimension and number of observation.

Keyword: Crop model evaluation, Index of agreement, Root mean square error, Model efficiency, Accuracy, Bias

MAIN

적요

작물모형 평가에 사용되거나 사용할 수 있는 9가지 지표를 소개하였으며 이들의 특징은 다음과 같다. efficiency of model (EF)와 index of agreement(d)은 dimension이 없고 관측수(n)에 의존적이지 않았으며, dimension에 대해서만 자유로운 것은 relative root mean square error (RRMSE), bias factor (Bf)와 accuracy factor (Af)이다. Root mean sqruar, mean error, mean absolute error들은 관측수와 dimension에 영향을 받기 때문에 판단 시 주의가 필요하다. 따라서 이들의 특징을 파악하여 목적에 맞게 모형의 성능을 파악하여야 한다.

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