참고문헌
- Cai, Y., W. Zheng, X. Zhang, L. Zhangzhong and X. Xue, 2019. Research on soil moisture prediction model based on deep learning. Plos one, 14(4), e0214508. doi:10.1371/journal.prone.0214508.
- Engman, E. T., 1991. Applications of microwave remote sensing of soil moisture for water resources and agriculture. Remote Sensing of Environment 35(2-3): 213-226. doi:10.1016/ 0034-4257(91)90013-V.
- Entekhabi D., E. G. Njoku, P. E. O'Neill, K. H. Kellogg, W. T. Crow, W. N. Edelstein, J. K. Entin, S. D. Goodman, T. J. Jackson, J. Johnson, J. Kimball, J. R. Piepmeier, R. D. Koster, N. Martin, K. C. McDonald, M. Moghaddam, S. Moran, R. Reichle, J. C. Shi, M. W. Spencer, S. W. Thurman, L. Tsang, and J. Van Zyl, 2010. The soil moisture active passive (SMAP) mission. Proceedings of the IEEE, 98(5): 704-716. doi:10.1109/JPROC.2010.2043918.
- Entekhabi, D., G. R. Asrar, A. K. Betts, K. J. Beven, R. L. Bras, and C. J. Duffy, 1999. An agenda for land surface hydrology research and a call for the second international hydrological decade. Bulletin of the American Meteorological Society 80(10): 2043-2058. doi:10.1175/1520-0477(1999)080<2043:AAFLSH>2.0.C0;2.
- Ines, A. V. M., B. P. Mohanty, and Y. Shin, 2013. An un-mixing algorithm for remotely sensed soil moisture. Water Resource Research 49(1): 408-425. doi:10.1029/2012WR012379.
- Kerr, Y. H., P. Waldteufel, J. P. Wigneron, J. A. M. J. Martinuzzi, J. Font, and M. Berger, 2001. Soil moisture retrieval from space: The soil moisture and ocean salinity (SMOS) mission. IEEE Transactions on Geoscience and Remote Sensing 39(8): 1729-1735. doi:10.1109/36.942551.
- Kim, Y. H., K. J. Kim, S. J. Lee, L. W. Kim, and Y. W. Yang, 2017. Deep learning-based retrieval of daily 500-m soil moisture for South Korea. Journal of the Korean Cartographic Association 17(3): 109-121. doi:10.16879/jkca.2017.17.3.109 (in Korean).
- Kingma, D. P., and J. Ba, 2014. Adam: A method for stochastic optimization. In Proc. 3rd International Conference for Learning Representations, arXiv:1412.6980.
- Lee, C. S., E. Sohn, J. D. Park, and J. D. Jang, 2018. Estimation of soil moisture using deep learning based on satellite data: A case study of South Korea. GIScience & Remote Sensing 56(1): 43-67. doi:10.1080/15481603.2018.1489943.
- McCulloch, W. S., and W. Pitts, 1943. A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics 5(4): 115-133. doi:10.1007/BF02478259.
- National Research Council, 1985. Reducing losses from landsliding in the United States. Washington, DC: The National Academies Press. doi:10.17226/19286.
- NGII (National Geographic Information Institute), 2008. The geography of Korea. The Ministry of Land, Transport and Maritime affairs, National Geographic Information Institute (in Korean).
- Shin, Y., and B. P. Mohanty, 2013. Development of a deterministic downscaling algorithm for remote sensing soil moisture footprint using soil and vegetation classifications. Water Resource Research 49(10): 6208-6228. doi:10.1002/wrce.20495.
- Song, X., G. Zhang, F. Liu, D. Li, Y. Zhao and J. Yang, 2016. Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model. Journal of Arid Land, 8(5), 734-748. doi:10.1007/s40333-016-0049-0.
- Srivastava, N., G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, 2014. Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 15(56): 1929-1958.
- Yang, I. T., K. S. Chun, J. K. Park, and S. Y. Lee, 2007. An estimation to landslide vulnerable area of rainfall condition using GIS. Journal of Korean Society for Geospatial Information System 15(1): 39-46.
- Zhang, H., L. Liao, K. M. Saravanan, P. Yin, and Y. Wei, 2019. DeepBindRG: A deep learning based method for estimating effective protein-ligand affinity. PeerJ 7: e7362. doi:10.7717/peerj.7362.