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The selection of radar merging by watershed for quantitative precipitation estimation: At the Han river basin
Journal of the Korean Data & Information Science Society 2022;33:1021-30
Published online November 30, 2022;
© 2022 Korean Data and Information Science Society.

Taeyong Kwon1 · Seongsim Yoon2 · Hongjoon Shin3 · Sanghoo Yoon4

1Department of Statistics, Daegu University
2Korea Institute of Civil Engineering and Building Technology
3Korea Hydro&Nuclear Power Co. Ltd.
4Department of Data Science, Daegu University
Correspondence to: This work was researched at Daegu university with funding from Korea Hydro&Nuclear Power Co., Ltd. (H21S031000).
1 Ph. D. candidate, Department of Statistics, Daegu University, Gyeongbuk 38453, Korea.
2 Senior researcher, Korea Institute of Civil Engineering and Building Technology, Gyeonggi, 10223, Korea.
3 Senior researcher, Korea Hydro&Nuclear Power Co. Ltd, Gyeongbuk, 38120, Korea.
4 Associate professor, Department of Data Science, Daegu University, Gyeongbuk 38453, Korea. E-mail:
Received September 6, 2022; Revised October 27, 2022; Accepted October 29, 2022.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Accurately estimated precipitation information is required for hydrological analysis and flood information production to reduce water disasters that increase due to extreme weather events. Since it is difficult to estimate precipitation over a wide range using only the ground-observed precipitation data for each point, the use of radar must be accompanied. For this purpose, in this study, ORCM (ordinary kriging conditional merging), which merges ground-observed rainfall and radar, and COCM (co-kriging conditional merging), which is a radar merging that considers the effect of altitude, were considered. In addition, the random forest algorithm was used to suggest the degree of contribution to the accuracy of quantitative precipitation estimation for each dual polarization variable. The study was conducted by selecting rain events that occurred in the Han River basin from July 2017 to October 2019. Prediction performance was identified by root mean square error and coefficient of determination, and the total precipitation and heavy precipitation were analyzed separately. As a result of the prediction, watersheds with excellent ORCM and COCM were selected, respectively, showing the need to consider the altitude. The main dual polarization variables of precipitation prediction were RZ in total precipitation and RKDP in heavy precipitation.
Keywords : Quantitative precipitation estimation, radar merging, random forest.