References
- Allen, R. G., L. S. Pereira, D. Raes, and M. Smith, 1998: Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper No. 56, Rome, Italy: United Nations FAO.
- Beysens, D., 2016: Estimationg dew yield worldwide from a few meteo data. Atmospheric Research 167, 146-155. https://doi.org/10.1016/j.atmosres.2015.07.018
- Beysens, D., M. Muselli, V. Nikolayev, R. Narhe, and I. Milimouk, 2005: Measurement and modeling of dew in island coastal and alpine areas. Atmospheric Research 73, 1-22. https://doi.org/10.1016/j.atmosres.2004.05.003
- Cosh, M. H., E. D. Kabela, B. Hornbuckle, M. L. Gleason, T. J. Jackson, and J. H. Prueger, 2009: Observation of dew amount using in situ and satellite measurements in an agricultural landscape. Agricultural and Forest Meteorology 149, 1082-1086. https://doi.org/10.1016/j.agrformet.2009.01.004
- Gauch, H. G., J. T. Hwang, and G. W. Fick, 2003: Model evaluation by comparison of model-based predictions and measured values. Agronomy Journal 95, 1442-1446. https://doi.org/10.2134/agronj2003.1442
- Gleason, M. L., S. E. Taylor, T. M. Loughin, and K. J. Koehler, 1994: Development and validation of an empirical model to estimate the duration of dew periods. Plant disease 78, 1011-1016.
- Gleason, M. L., 2007: Validation of weather inputs for disease warning systems. American Phytopathological Society 97(7), 147.
- Hinton, G. E., S. Osindero, and Y. W. Teh, 2006: A fast learning algorithm for deep belief nets. Neural computation 18(7), 1527-1554. https://doi.org/10.1162/neco.2006.18.7.1527
- Hong, K. O., M. S. Suh, and D. K. Rha, 2006: Temporal and spatial variations of precipitation in South Korea for recent 30 years (1976-2005) and geographic environments. Journal of Korean Earth Sciences Society 27(4), 433-449.
- Kim, K. S., S. E. Taylor, M. L. Gleason, R. Villalobos, and L. F. Arauz, 2005: Estimation of leaf wetness duration using empirical models in northwestern Costa Rica. Agricultural and Forest Meteorology 129, 53-67. https://doi.org/10.1016/j.agrformet.2004.11.009
- Kim, K. S., S. E. Taylor, M. L. Gleason, and K. J. Koehler, 2002: Model to enhance site-specific estimation of leaf wetness duration. Plant disease 86, 179-185. https://doi.org/10.1094/PDIS.2002.86.2.179
- Kim, S. S., S. M. Jang, H. J. Baek, H. Y. Choi, and W. T. Kwon, 2006: Climatological variability of temperature and precipitation in Jeju. Journal of Korean Earth Sciences Society 27(2), 188-197.
- Klemm, O., C. Milford, M. A. Sutton, G. Spindler, and E. Van Putten, 2002: A climatology of leaf surface wetness. Theoretical and Applied Climatology 71, 107-117. https://doi.org/10.1007/s704-002-8211-5
- LeCun, Y., Y. Bengio, and G. Hinton, 2015: Deep learning. nature 521(7553), 436-444. https://doi.org/10.1038/nature14539
- Lee, H., J. B. Jee, J. S. Min, S. Kim, and J. H. Chae, 2018: Analysis of meteorological and radiation characteristics using WISE observation data. Journal of Korean Earth Sciences Society 39(1), 89-102. https://doi.org/10.5467/JKESS.2018.39.1.89
- Lee, Y. J., and J. Nang, 2016: A personal video event classification method based on multi-modalities by DNN-Learning. Journal of KIISE 43(11), 1281-1297. https://doi.org/10.5626/JOK.2016.43.11.1281
- Luo, W., and J. Goudriaan, 2004: Estimating dew formation in rice, using seasonally averaged dial patterns of weather variables. NJAS wageningen journal of life sciences 51, 391-406. https://doi.org/10.1016/S1573-5214(04)80004-6
- Maestre-Valero, J. F., R. Ragab, V. Martinez-Alvarez, and A. Baille, 2012: Estimation of dew yield from radiative condensers by means of an energy balance model. Journal of Hydrology 460, 103-109.
- Park, J. H., D. H. Shin, and C. B. Kim, 2017: Deep learning model for electric power demand prediction using special day separation and prediction elements extension. Journal of Advanced Navigation Technology 21(4), 365-370. https://doi.org/10.12673/JANT.2017.21.4.365
- Park, J. S., K. R. Kim, M. Kang, and B. J. Kim, 2017: The influence of evaporation from a stream on fog events in the middle Nakdong River. Journal of Korean Earth Sciences Society 38(6), 395-404. https://doi.org/10.5467/JKESS.2017.38.6.395
- Rowlandson, T., M. Gleason, P. Sentelhas, T. Gillespie, C. Thomas, and B. Hornbuckle, 2015: Reconsidering leaf wetness duration determination for plant disease management. Plant Disease 99, 310-319. https://doi.org/10.1094/PDIS-05-14-0529-FE
- Sentelhas, P. C., T. J. Gillespie, M. L. Gleason, J. E. B. Monteiro, J. R. M. Pezzopane, and M. J. Pedro, 2006: Evaluation of a Penman-Monteith approach to provide "reference" and crop canopy leaf wetness duration estimates. Agricultural and Forest Meteorology 141, 105-117. https://doi.org/10.1016/j.agrformet.2006.09.010
- Sentelhas, P. C., A. Dalla Marta, S. Orlandini, E. A. Santos, T. J. Gillespie, and M. L. Gleason, 2008: Suitability of relative humidity as an estimator of leaf wetness duration. Agricultural and forest meteorology 148, 392-400. https://doi.org/10.1016/j.agrformet.2007.09.011
- Shin, D. H., and C. B. Kim, 2018: Short term forecast model for solar power generation using RNN-LSTM. Journal of Advanced Navigation Technology 22(3), 233-239. (in Korean) https://doi.org/10.12673/JANT.2018.22.3.233
- Montone, V. O., C. W. Fraisse, N. A. Peres, P. C. Sentelhas, M. Gleason, M. Ellis, and G. Schnabel, 2016: Evaluation of leaf wetness duration models for operational use in strawberry disease-warning systems in four US states. International journal of biometeorology 60, 1761-1774. https://doi.org/10.1007/s00484-016-1165-4
- Veronica, N. S., W. F. Clyde, A. P. Natalia, C. C. James, and C. Amy, 2010: Spatial variability of leaf wetness duration in citrus canopies. Proceedings of the Florida State Horticultural Society 123, 49- 55.
- Wallach, D., and B. Goffinet, 1989: Mean squared error of prediction as a criterion for evaluating and comparing system models. Ecological Modeling 44, 299-306. https://doi.org/10.1016/0304-3800(89)90035-5
- Wieland, R., W. Mirschel, K. Groth, A. Pechenick, and K. Fukuda, 2011: A new method for semi-automatic fuzzy training and its application in environmental modeling. Environmental modeling & software 26, 1568-1573. https://doi.org/10.1016/j.envsoft.2011.07.017