한국농공학회논문집 (Journal of The Korean Society of Agricultural Engineers)

  • 제61권1호
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  • Pages.95-105
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  • 2019
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  • 1738-3692(pISSN)
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  • 2093-7709(eISSN)
한국농공학회 (The Korean Society of Agricultural Engineers)
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인공신경망모형을 이용한 기온기반 기준증발산량 산정

Estimating Reference Crop Evapotranspiration Using Artificial Neural Network and Temperature-based Climatic Data

  • Lee, Sung-Hack (Department of Rural Systems Engineering, Seoul National University) ;
  • Kim, Maga (Department of Rural Systems Engineering, Seoul National University) ;
  • Choi, Jin-Yong (Department of Rural Systems Engineering, Research Institute of Agriculture and Life Sciences, Seoul National University) ;
  • Bang, Jehong (Department of Rural Systems Engineering, Seoul National University)
  • 투고 : 2017.04.10
  • 심사 : 2018.11.08
  • 발행 : 2019.01.31
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초록

Evapotranpiration (ET) is one of the important factor in Hydrological cycle and irrigation planning. In this study, temperature-based artificial neural network (ANN) model for daily reference crop ET estimation was developed and compared with reference crop evapotranpiration ($ET_0$) from FAO-56 Penman-Monteith method (FAO-56 PM) and parameter regionalized Hargreaves method. The ANN model was trained and tested for 10 weather stations (5 inland stations and 5 costal stations) and two input climate factors, maximum temperature ($T_{max}$), minimum temperature ($T_{min}$), and extraterrestrial radiation (RA) were used for training and validation of temperature-based ANN model. Monthly reference ET by the ANN model also compared with parameter regionalized Hargreaves method for ANN model applicability evaluation. The ANN model evapotranspiration demonstrated more accordance to FAO-56 PM evapotranspiration than the $ET_0$ from parameter regionalized Hargreaves method(R-Hargreaves). The results of this study proposed that daily reference crop ET estimated by the ANN model could be used in the condition of no sufficient climate data.

키워드

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Fig. 1 Structure of typical multi-layer neural network

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Fig. 2 Scatter plots comparing calculated daily ET0 by FAO-56 PM method and simulated ET0 by artificial neural network method for training data

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Fig. 3 Scatter plots comparing calculated daily ET0 by FAO-56 PM method and simulated ET0 by artificial neural network method for validation data

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Fig. 4 Scatter plots comparing calculated monthly ET0 by FAO-56 PM method and simulated ET0 by artificial neural network method (2011∼2015)

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Fig. 5 Scatter plots comparing calculated ET0 by FAO-56 PM method and calculated ET0 by R-Hargreaves method (2011-2015)

Table 1 Description of weather stations used in this study

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Table 2 Number of nodes in hidden layer and calibration and validation statics for each stations(observation start time∼2010)

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Table 3 Statics of monthly evapotranspiration estimated by ANN and adjusted Hargreaves method for each stations during test period(2011∼2015)

NGHHCI_2019_v61n1_95_t0003.png 이미지

참고문헌

  1. Allen, R. G., M. Smith, A. Perroer, and L. S. Preira, 1994. An update for the calculation of reference evapotranspiration. ICID Bull 43(2): 35-92.
  2. APEC Cliamte Center, Clipped CMIP5 data, http://adss.apcc21.org/DataSet/CMIP5/cmip5,jsp. Accessed 31 Mar. 2017.
  3. Gocic, M., and S. Trajkovic, 2010. Software for estimating reference evapotranspiration using limited weahter data. Computers and Electronics in Agriculture 71: 158-162. doi:10.1016/j.compag.2010.01.003.
  4. Hargreaves, G. H., 1975. Moisture availability and crop production. Transactions of ASAE 18(5): 980-984. doi:10.13031/2013.36722.
  5. Hargreaves, H. G., and A. Z. Samani, 1985. Reference crop evapotranspiration from temperature. Applied Engineering in Agriculture 1(2): 96-99. doi:10.13031/2013.26773.
  6. Jesen, M. E. (Ed), 1974. Consumptive use of water and irrigation water requirements. Rep. Tech, Comm. on Irrigation, p. 277.
  7. Kang, M. S., and S. W. Park, 2003. Short-term flood forecasting using artificial neural networks. Journal of the Korean Society of Agricultural Engineers 45(2): 45-57 (in Korean).
  8. Kisi, O., 2009. Daily pan evaporation modelling using multi-layer perceptions and radial basis neural networks. Hydrological Processes 23: 213-223. doi:10.1002/hyp.7126.
  9. Kumar, M., N. S. Raghuwanshi, and R. Singh, 2011. Artificial neural networks approach in evapotranspiration modelling: a review. Irrigation Science 29: 11-25. doi:10.1007/s00271-010-0230-8.
  10. Kumar, M., N. S. Ranghuwanshi, S. Singh, W. W. Wallender, and W. O. Pruitt, 2002. Estimating evapotranspiration using artificial neural network. Journal of Irrigation and Drainage 128(4): 224-233. doi:10.1061/(ASCE)0733-9437(2002)128:4(224).
  11. Lee, E. J., M. S. Kang, J. A. Park, J. Y. Choi, and S. W. Park, 2010. Estimation of future reference crop evapotranspiration using artificial neural network. Journal of Korean Society of Agricultural Engineers 52(5): 1-9 (in Korean). doi:10.5389/KSAE.2010.52.5.001.
  12. Makridakis, S., S. C. Wheelwright, and R. J. Hyndman, 1998. Forecasting-mothods and application (Third Ed.). Wiley, New York, pp. 42-50.
  13. McVicker, R., 1982. The effects of model complexity on ther predictive accuracy of soil moisture accounting models M.S. Thesis, Utah State University, Logan, Utah.
  14. Moon, J. W., C. G. Jung, and D. R. Lee, 2013. Parameter regionalization of Hargreaves equation based on climatological characteristics in Korea. Journal of Korea Water Resources Association 46(9): 933-946. doi:10.3741/JKWRA.2013.46.9.933.
  15. Oh, S. K., 2008. Pattern recognition. Kyobo Moongo, Seoul, p. 98.
  16. Salih, A. M. A., and U. Sendil, 1984. Evapotranspiration under extremely arid climates. Journal of Irrigation and Drainage 110(3): 289-303. doi:10.1061/(ASCE)0733-9437(1984)110:3(289).
  17. Shih, S. F., 1984. Data requirement for evapotranspiration estimation. Journal of Irrigation and Drainage 110(3): 263-274. doi:10.1061/(ASCE)0733-9437(1984)110:3(263).
  18. Sudheer, K. P., A. K. Gosain, and K. S. Ramasatri, 2003. Estimating actual evapotranspiration from limited climatic data using neural computing technique. Journal of Irrigation and Drainage 129(3): 214-218. doi:10.1080/09715010.2009.10514929.
  19. Trajkovic, S., 2005. Temperature-based approaches for estimating reference evapotranspiration. Journal of Irrigation and Drainage 131(4): 316-323. doi:10.1061/(ASCE)0733-9437(2005)131:4(316).
  20. Trajkovic, S., and S. Kolakovic, 2003. Estimating reference evapotranspiration using limited weather data. Journal of Irrigation and Drainage 45(2): 45-57. doi:10.1061/(ASCE)IR.1943-4774.0000094.
  21. Wang, Y. M., S. Traore, and T. Kerh, 2008. Neural network approach for estimating reference evapotranspirtion form limited climatic data in Burkina Faso. WSEA Transactions on Computers 6(7): 704-713.
  22. Zanetti, S. S., E. F. Sousa, W. P. S. Oliveira, F. T. Almeida, and S. Bernardo, 2007. Estimating evapotranspiration using artificial neural network and minimum climatological data. Jousrnal of Irrigation and Drainage 133(2): 83-89. doi:10.1061/(ASCE)0733-9437(2007)133:2(83).