대한지리학회지 (Journal of the Korean Geographical Society)

대한지리학회 (The Korean Geographical Society)
권호 표지

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
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

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

참고문헌

  1. Aubert, D., Loumagne, C., and Oudin, L., 2003, Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall-runoff model, Journal of Hydrology, 280(1-4), 145-161. https://doi.org/10.1016/S0022-1694(03)00229-4
  2. Coelho, L. S., Freire, R. Z., Santos, G. H., and Mendes, N., 2009, Identification of temperature and moisture content fields using a combined neural network and clustering method approach, International Communications in Heat and Mass Transfer, 36(4), 304-313. https://doi.org/10.1016/j.icheatmasstransfer.2009.01.012
  3. Frate, F. D., Ferrazzoli, P., and Schiavon, G., 2003, Retrieving soil moisture and agricultural variables by microwave radiometry using neural networks, Remote Sensing of Environment, 84(2), 174-183. https://doi.org/10.1016/S0034-4257(02)00105-0
  4. Gautam, M. R., Watanabe, K., and Ohno, H., 2004, Effect of bridge construction on floodplain hydrology-assessment by using monitored data and artificial neural network models, Journal of Hydrology, 292(1-4), 182-197.
  5. Hong, W. Y., Park, M. J., Park, J. Y., Park, G. A., and Kim S. J., 2009, The correlation analysis between SWAT predicted forest soil moisture and MODIS NDVI image, The Korean Society of Remote Sensing 2009 spring Conference, 111-115 (in Korean).
  6. Kim, G. S., 2007, The soil moisture analysis for watershed management(I): Research trends of soil moisture observation (김광섭, 2007, 유역관리를 위한 토양수분 분석(I): 토양수분 관측 연구동향), Magazine of Korea Water Resources Association, 40(1), 62-71 (in Korean).
  7. Kim, Y. S., Yang, J. L., Lee, H. S., and Koh, D. K., 2007, Water resources experimental watershed in Yongdam dam basin (김영성.양재린.이현석.고덕구, 2007, 용담댐 수자원 시험유역), Magazine of Korea Water Resources Association, 40(6), 48-53 (in Korean).
  8. Kyoung, M. S., Kim, B. S., and Kim, H. S., 2009, Assessment of climate change effect on drought in Korea, Korea Water Resources Association 2009 Conference, 1457-1461 (in Korean).
  9. Lee, W. H., Jun, K. W., Kim, J. G., and Yeon, I.-S., 2007, Construction of system for water quality forecasting at Dalchun using neural network model, Journal of the Korean Society of Water and Wastewater, 21(3), 305-314 (in Korean).
  10. Maier, H. R. and Dandy, G. C., 1996, The use of artificial neural networks for the prediction of water quality parameters, Water Resources Research, 32(4), 1013-1022. https://doi.org/10.1029/96WR03529
  11. Minns, A. W. and Hall, M. J., 1996, Artificial neural networks as rainfall-runoff models, Hydrological Sciences Journal, 41(3), 399-417. https://doi.org/10.1080/02626669609491511
  12. Ohkubo, A., Mohamed, M., and Niijima, K., 1998, A soil moisture map generated from satellite data by using domains of attraction in neural networks, International Conference on Neural Information Processing, 356-359.
  13. Park, D. K., Yi, S. K., and Cho, W.-C., 2002, Rainfall estimation at an ungaged point using artificial neural network theory, Korean Society of Civil Engineers Conference, 1242-1245 (in Korean).
  14. Park, E. J., Hwang, C. S., and Seong, J. C., 2002, The analysis of drought susceptibility using soil moisture information and spatial factors involved in satellite imagery, The Journal of GIS Association of Korea, 10(3), 481-492 (in Korean).
  15. Pierdicca, N., Pulvirenti, L., and Bignami, C., 2010, Soil moisture estimation over vegetated terrains using multitemporal remote sensing data, Remote Sensing of Environment, 114(2), 440-448. https://doi.org/10.1016/j.rse.2009.10.001
  16. Qiu, Y., Fu, B., Wang, J., and Chen, L., 2003, Spatiotemporal prediction of soil moisture content using multiple-linear regression in a small catchment of the Loess Plateau, China, CATENA, 54(1-2), 173-195. https://doi.org/10.1016/S0341-8162(03)00064-X
  17. Rouse, J. W., Haas, R. H., Schell, J. A., and Deering, D. W., 1974, Monitoring vegetation systems in the Great Plains with ERTS, Third Earth Resources Technology Satellite-1 Symposium, 1, 309-317.
  18. Rumelhart, D. E., Hinton, G. E., and Williams, R. J., 1986, Learning internal representations by Error Propagation. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press Cambridge, MA, USA, 1, 318-362.
  19. Santanello Jr., J. A., Peters-Lidard, C. D., Garcia, M. E., Mocko, D. M., Tischler, M. A., Moran, M. S., and Thoma, D. P., 2007, Using remotely-sensed estimates of soil moisture to infer soil texture and hydraulic properties across a semi-arid watershed, Remote Sensing of Environment, 110(1), 79-97. https://doi.org/10.1016/j.rse.2007.02.007
  20. Wang, H., Li, X., Long, H., Xu, X., and Bao, Y., 2010, Monitoring the effects of land use and cover type changes on soil moieture using remotesensing data: A case study in China's Yongding River basin, CATENA, 82(3), 135-145. https://doi.org/10.1016/j.catena.2010.05.008
  21. Wigneron, J. P., Calvet, J. C., Pellarin, T., Van de Griend, A. A., Berger, M., and Ferrazzoli, P., 2003, Retrieving near-surface soil moisture from microwave radiometric observations: current status and future plans, Remote Sensing of Environment, 85(4), 489-506. https://doi.org/10.1016/S0034-4257(03)00051-8
  22. Zealand, C. M., Burn, D. H., and Simonovic, S. P., 1999, Short term streamflow forecasting using artificial neural network, Journal of Hydrology, 214(1-4), 32-48. https://doi.org/10.1016/S0022-1694(98)00242-X
  23. Zribi, M., Baghdadi, N., Holah, N., and Fafin, O., 2005, New nethodology for soil surface moisture estimation and its application to ENVISAT-ASAR multi-incidence data inversion, Remote Sensing of Environment, 96(3-4), 485-496. https://doi.org/10.1016/j.rse.2005.04.005