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2018 Vol.51, Issue 5
Assessing applicability of self-organizing map for regional rainfall frequency analysis in South Korea

Self-organizing map을 이용한 강우 지역빈도해석의 지역구분 및 적용성 검토

Hyunjun Ahna
,
Ju-Young Shina
,
Changsam Jeongb
,
Jun-Haeng Heoa*
안 현준a
,
신 주영a
,
정 창삼b
,
허 준행a*
aDepartment of Civil and Environmental Engineering, Yonsei University
bDepartment of Civil Engineering, Induk University
a연세대학교 토목환경공학과
b인덕대학교 토목공학과
*교신저자. *Corresponding Author. 
31 May 2018. pp. 383-393
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Abstract

The regional frequency analysis is the method which uses not only sample of target station but also sample of neighborhood stations in which are classified as hydrological homogeneous regions. Consequently, identification of homogeneous regions is a very important process in regional frequency analysis. In this study, homogeneous regions for regional frequency analysis of precipitation were identified by the self-organizing map (SOM) which is one of the artificial neural network. Geographical information and hourly rainfall data set were used in order to perform the SOM. Quantization error and topographic error were computed for identifying the optimal SOM map. As a result, the SOM model organized by 7 × 6 array with 42 nodes was selected and the selected stations were classified into 6 clusters for rainfall regional frequency analysis. According to results of the heterogeneity measure, all 6 clusters were identified as homogeneous regions and showed more homogeneous regions compared with the result of previous study.

Keywords
Self-organizing map
cluster analysis
Regional frequency analysis
Heterogeneity measure

지역빈도해석은 대상 지점과 수문학적 동질성을 만족하는 주변 지점을 하나의 지역으로 보고 빈도해석을 수행하는 방법이다. 따라서 동질한 지역의 구분은 지역빈도해석에 있어서 가장 중요한 가정이라고 할 수 있다. 이에 본 연구에서는 인공신경망 기법중 하나인 자기조직화지도(self- organizing map, SOM) 기법을 활용하여 강우 지역빈도해석을 위한 동질 강수 지역을 구분하였다. 지역구분 인자로는 지형 정보와 시 단위 강우자료를 활용하였다. 최적 SOM 지도 구성을 위해 정량적 오차와 위상관계 오차를 활용하였다. 그 결과 7 × 6 배열의 42개의 노드를 갖는 모형을 선정하였고 최종적으로 강우 지역빈도해석을 위해 6개의 군집으로 구분하였다. 동질성 검토 결과 6개의 군집 모두 동질한 지역으로 나타났으며 기존의 유사하게 구분된 지역들과 이질성 척도를 비교하였을 때 좀 더 안정적인 지역 구분결과를 나타내는 것을 확인하였다.

키워드
자기조직화지도
군집분석
지역빈도해석
이질성 척도
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Information
  • Publisher :KOREA WATER RESOURECES ASSOCIATION
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 51
  • No :5
  • Pages :383-393
  • Received Date : 2018-01-15
  • Revised Date : 2018-02-02
  • Accepted Date : 2018-02-02
  • DOI :https://doi.org/10.3741/JKWRA.2018.51.5.383
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