인공신경망을 이용한 용담댐 유역 공간 토양수분 분포도 산정

Estimation of Spatial Distribution of Soil Moisture at Yongdam Dam Watershed Using Artificial Neural Networks

  • 박정아 (경북대학교 공간정보학과) ;
  • 김광섭 (경북대학교 건축.토목공학부)
  • Park, Jung-A (Department of Spatial Information, Kyungpook National University) ;
  • Kim, Gwang-Seob (Department of Architecture and Civil Engineering, Kyungpook National University)
  • 투고 : 2011.02.18
  • 심사 : 2011.06.16
  • 발행 : 2011.06.30

초록

본 연구에서는 지상관측 토양수분, 강수량, 지면온도 및 MODIS NDVI와 인공신경망모형을 이용하여 토양수분 공간분포 산정 모형을 제안하였으며, 신뢰성 높은 토양수분 관측 자료를 보유한 용담댐 유역에 대하여 모형의 적용성을 검증하였다. 토양수분 산정모형의 학습에 사용된 주천, 부귀, 상전의 3개 지점의 경우 약 0.9353의 상관계수와 약 1.4957%의 평균제곱근오차를 보여주며, 검증지점으로 사용된 천천2의 경우에는 약 0.8215의 상관계수와 약 4.2077%의 평균제곱근오차를 보여 토양수분 산정모형의 적용가능성이 높다고 판단된다. 인공위성으로부터 관측된 광역의 식생정보와 자료간의 비선형 상관특성을 잘 구현하는 인공신경망을 활용하여 수립된 토양수분 산정모형을 이용하여 용담댐 유역의 토양수분 공간분포도를 산정한 결과, 용담댐 유역의 대부분을 차지하고 있는 산림지역의 토양수분이 다른 지역에 비하여 높은 수치를 보여주는 토양수분의 분포를 보여주었다. 본 연구를 통해 제시된 토양수분 산정 방법은 광역 토양수분 산정에 유용한 접근법으로 판단된다.

In this study, a soil moisture estimation model was proposed using the ground observation data of soil moisture, precipitation, surface temperature, MODIS NDVI and artificial neural networks. The model was calibrated and verified on the Yongdam dam watershed which has reliable ground soil moisture networks. The test statistics of calibration sites, Jucheon, Bugui, Sangjeon, showed that the correlation coefficients between observations and estimations are about 0.9353 and RMSE is about 1.4957%. Also that of the verification site, Cheoncheon2, showed that the correlation coefficient is about 0.8215 and RMSE is about 4.2077%. The soil moisture estimation model was applied to estimate the spatial distribution of soil moisture in the Yongdam dam watershed and results showed improved spatial soil moisture distribution since the model used satellite information of NDVI and artificial neural networks which can represent the nonlinear relationships between data well. The model should be useful to estimate wide range soil moisture information.

키워드

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