한국농공학회논문집 (Journal of The Korean Society of Agricultural Engineers)

  • 제61권6호
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  • Pages.111-121
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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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역전파 신경망 모델을 이용한 기준 작물 증발산량 산정

Estimation of Reference Crop Evapotranspiration Using Backpropagation Neural Network Model

  • Kim, Minyoung (Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA)) ;
  • Choi, Yonghun (Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA)) ;
  • O'Shaughnessy, Susan (Conservation and Production Research Laboratory, United States Department of Agriculture, Agricultural Research Service (USDA-ARS)) ;
  • Colaizzi, Paul (Conservation and Production Research Laboratory, United States Department of Agriculture, Agricultural Research Service (USDA-ARS)) ;
  • Kim, Youngjin (Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA)) ;
  • Jeon, Jonggil (Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA)) ;
  • Lee, Sangbong (Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA))
  • 투고 : 2019.10.01
  • 심사 : 2019.11.08
  • 발행 : 2019.11.30
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초록

작물 증발산량은 수자원 계획 및 관리, 물수지 분석, 작물 관개 계획 및 생산량 추정 등에 널리 활용되고 있으며, 특히 FAO에서 공인한 Penman-Monteith식 (FAO 56-PM)은 잠재 증발산량 산정을 위한 표준방법으로 많이 사용되고 있다. Penman-Monteith식을 이용한 잠재증발산량 산정은 최소온도, 평균온도, 최대온도, 상대습도, 풍속과 일사량인 6가지 항목에 대한 시계열 자료가 필요한데, 결측 또는 미계측된 경우에는 사용이 어려운 단점을 가지고 있다. 따라서, 본 연구에서는 역전파 신경망(BPNN) 모델을 이용해서 6개 미만의 기상항목으로도 잠재증발산량이 추정가능한지를 확인하였다. 여섯 가지 기상항목을 각각 1~6개의 조합으로 입력자료를 구성하고, BPNN 모델을 이용해서 학습, 검증 및 테스트를 한 결과, 입력 자료가 많아질수록 좋은 결과가 산출되었으며, 일사량, 최대온도와 상대습도만으로도 결정계수($R^2$)가 0.94정도로 비교적 높은 예측결과를 얻을 수 있었다. 또한 산정 오차를 줄이고, 항목간의 상관관계를 높이기 위해서는 역전파 신경망 구조의 적절한 선택이 중요한 것으로 확인되었다. 역전파 신경망 모델을 사용하면 요구되는 기상 항목과 데이터의 양에 대한 제약 없이 예측이 가능할 수 있기 때문에 기준 증발산량 산정에 유용하게 활용될 수 있을 것이며 향후 작물 재배를 위한 적정 관개계획 수립에도 유용하게 사용될 것이라 사료된다.

Evapotranspiration (ET) of vegetation is one of the major components of the hydrologic cycle, and its accurate estimation is important for hydrologic water balance, irrigation management, crop yield simulation, and water resources planning and management. For agricultural crops, ET is often calculated in terms of a short or tall crop reference, such as well-watered, clipped grass (reference crop evapotranspiration, $ET_o$). The Penman-Monteith equation recommended by FAO (FAO 56-PM) has been accepted by researchers and practitioners, as the sole $ET_o$ method. However, its accuracy is contingent on high quality measurements of four meteorological variables, and its use has been limited by incomplete and/or inaccurate input data. Therefore, this study evaluated the applicability of Backpropagation Neural Network (BPNN) model for estimating $ET_o$ from less meteorological data than required by the FAO 56-PM. A total of six meteorological inputs, minimum temperature, average temperature, maximum temperature, relative humidity, wind speed and solar radiation, were divided into a series of input groups (a combination of one, two, three, four, five and six variables) and each combination of different meteorological dataset was evaluated for its level of accuracy in estimating $ET_o$. The overall findings of this study indicated that $ET_o$ could be reasonably estimated using less than all six meteorological data using BPNN. In addition, it was shown that the proper choice of neural network architecture could not only minimize the computational error, but also maximize the relationship between dependent and independent variables. The findings of this study would be of use in instances where data availability and/or accuracy are limited.

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참고문헌

  1. Abedi-Koupai, J., M. J. Amiri, and S. Eslamian, 2009. Comparison of artificial neural network and physically-based models for estimating of reference evapotranspiration in greenhouse. Australian Journal of Basic and Applied Sciences 3(3): 2528-2535.
  2. 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, FAO, Rome.
  3. Allen, R. G., W. O. Pruitt, J. L. Wright, T. A. Howell, F. Ventura, R. Snyder, D. Itenfisu, P. Steduto, J. Berengena, J. B. Yrisarry, M. Smith, L. S. Pereira, D. Raes, A. Perrier, A. Alves, I. Walter, and R. Elliot, 2006. A recommendation on standardized surface resistance for hourly calculation of reference $ET_o$ by the FAO 56 Penman-Monteith method. Agricultural Water Management 81(1-2): 1-22. https://doi.org/10.1016/j.agwat.2005.03.007
  4. Allen, R. G., L. S. Pereira, T. A. Howell, and M. E. Jensen, 2011. Evapotranspiration information reporting: II. Recommended documentation. Agricultural Water Management 98: 921-929 https://doi.org/10.1016/j.agwat.2010.12.016
  5. Basheer I. A., and M. Hajmeer, 2000. Artificial neural networks: Fundamentals, computing, design, and application. J. Microbiol. Methods 43(1): 3-31. https://doi.org/10.1016/S0167-7012(00)00201-3
  6. Benzaghta, M. A., T. A. Mohammed, and A. I. Ekhmaj, 2012. Prediction of evaporation from Algardabiya reservoir. Libyan Agriculture Research Center Journal International 3: 120-128. doi:10.5829/idosi.larcji.2012.3.3.1205.
  7. Choi, Y., M. Kim, S. O'Shaughnessy, J. Jeon, Y. Kim, and W. Song, 2018. Comparison of artificial neural network and empirical models to determine daily reference evapotranspiration. Journal of the Korean Society of Agricultural Engineers 60(6): 43-54. doi:10.5389/KSAE.2018.60.6.043.
  8. Coulibaly, P., F. Anctil, R. Aravena, B. Bobee, 2001. Artificial neural network modeling of water table depth fluctuations. Water Resources Research 37(4): 885-896. doi:10.1029/2000WR900368.
  9. Dai, X., H. Shi, Y. Li, Z. Ouyang, and Z. Huo, 2009. Artificial neural network models for estimating regional reference evapotranspiration based on climate factors. Hydrological Processes 23: 442-450. doi:10.1002/hyp.7153.
  10. Djman, K., K. Lombard, K. Komlan, and S. Allen, 2018. Variability of the ratio of alfalfa to grass reference evapotranspiration under semiarid climate. Irrigation & Drainage Systems Engineering 7(204): 1-6. doi:10.4172/2168-9768.1000204.
  11. Drexler, J. Z., R. L. Snyder, D. Spano, and U. K. T. Paw, 2004. A review of models and micrometeorological methods used to estimate wetland evapotranspiration. Hydrological Processes 18(11): 2071-2101. doi:10.1002/hyp.1462.
  12. George, B. A., B. R. S. Reddy, N. Raghuwanshi, and W. W. Wallender, 2002. Decision support system for estimating reference evapotranspiration. Journal of Irrigation and Drainage Engineering 128(1): 1-10. doi:10.106/ASCE.0733-9437.
  13. Goel, A., 2009. ANN based modeling for prediction of evaporation in reservoirs (Research Note). International Journal of Engineering, Transactions A: Basics 22(4): 351-358.
  14. Haykin, S., 1998. Neural networks: A comprehensive foundation. Prentice-Hall, Englewood Cliffs.
  15. Itenfisu, D., R. L. Elliott, R. G. Allen, and I. A. Walter, 2003. Comparison of reference evapotranspiration calculations as part of the ASCE standardization effort. Journal of Irrigation and Drainage Engineering 129(6): 440-448. doi:10.1061/ASCE. 0733-9437.
  16. Jain S. K., A Sarkar, and V. Garg, 2008. Impact of declining trend of flow on Harike Wetland, India. Water Resources Management 22(4): 409-421. doi:10.1007/s11269-007-9169-9.
  17. Jennifer, M. J., and R. S. Sudheer, 2001. Evaluation of reference evapotranspiration methodologies and AFSIRS crop water use simulation model. Final report, Division of Water Supply Management, St. Johns River Water Manag, Dist., Palatka, Florida.
  18. Jensen, M. E., R. D. Burman, and R. G. Allen, 1990. Crop and irrigation water requirements. Manual and Reports on Engineering Practice No. 70, ASCE, New York.
  19. Kecman, V., 2001. Learning and soft computing. London, England: MIT press.
  20. Khoob, A. R., 2008. Comparative study of Hargreaves's and artificial neural network's methodologies in estimating reference evapotranspiration in a semiarid environment. Irrigation Sci. 26(3): 253-259. doi:10.1007/s00271-007-0090-z.
  21. Kim, M., C. Y. Choi, and C. P. Gerba, 2008. Source tracking of microbial intrusion in water system using artificial neural networks. Water Research 42(4-5): 1308-1314. doi:10.1016/j.watres.2007.09.032.
  22. Laaboudi A., B. Mouhouche, and B. Draoui, 2012. Conceptual reference evapotranspiration models for different time steps. Journal of Petroleum & Environmental Biotechnology 3(4): 1-8. doi:10.4172/2157-7463.1000123.
  23. Landeras G., A. Ortiz-Barredo, and J. J. Lopez, 2008. Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain). Agricultural Water Management 95: 553-565. doi:10.1016/k/agwat/2007.12.011.
  24. 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 networks. Journal of the Korean Society of Agricultural Engineers 52(5): 1-9. doi:10.5389/KSAE.2010.52.5.001.
  25. Lu, Y., D. Ma, X. Chen, and J. Zhang, 2018, A simple method for estimating field crop evapotranspiration from Pot Experiments. Water 10: 1-19. doi:10.3390/210121823.
  26. Maier, H. R., and G. C. Dandy, 2000. Neural networks for the prediction and forecasting of water resources variables: A review of modeling issues and applications. Environmental Model. Software 15: 101-124. doi:10.1016/S1364-8152(99)00007-9.
  27. Mia, M. M., S. K. Biswas, and M. C. Urmi, 2015. An algorithm for training multilayer perceptron (MLP) For image reconstruction using neural network without overfitting. IJSTR 10: 271-275.
  28. Palayasoot, P., 1965. Estimation of pan evaporation and potential evapotranspiration of rice in the central plain of Thailand by using various formulas based on climatological data. M. S. Thesis, College of Engineering, Utah State University, Logan.
  29. Parisi, S., L. Mariani, G. Cola, and T. Maggiore, 2009. Mini-lysimeters evapotranspiration measurements on suburban environment. Italian Journal of Agrometeorologoy 3: 13-16.
  30. Smith, M., R. G., Allen, J. L. Monteith, A. Perrier, L. Pereira, and A. Segeren, 1992. Report of the expert consultation on procedures for revision of FAO guidelines for prediction of crop water requirements. UN-FAO, Rome, Italy, 54p.
  31. Sudheer, K. P., and S. K. Jain, 2003. Radial basis function neural networks for modeling stage discharge relationship. J. Hydrol. Eng. 8(3): 161-164. https://doi.org/10.1061/(ASCE)1084-0699(2003)8:3(161)
  32. Traore, A., H. H. Tamboura, A. Kabore, L.J. Royo, I. Fernandez, I. Alvarez, M. Sangare, D. Bouchel, J. P. Poivey, L. Sawadogo, and F. Goyache, 2008. Multivariate analyses on morphological traits in Burkina Faso goat. Arch Anim Breed 51: 588-600. doi:10.1016/j.smallrumres.2008.09.011.
  33. Vyas, K. N., and R. Subbaiah, 2016. Application of artificial neural network approach for estimating reference evapotranspiration. Current World Environment 11(2): 637-647. doi:10.12944/CWE.11.2.36.
  34. Zanetti, S. S., E. F. Sousa, V. P. S. Oliveira, F. T. Almeida, and S. Bernardo, 2007. Estimating evapotranspiration using artificial neural network and minimum climatological data. Journal of Irrigation and Drainage Engineering 133(2): 83-89. doi:10.1061/(ASCE)0733-9437(2002)128:4(224).
  35. Wu, W., G. C. Dandy, and H. R. Maier, 2014. Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modeling. Environ. Model. Softw. 54: 108-127. doi:10.1016/j.envsoft.2013.12.016.