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http://dx.doi.org/10.5389/KSAE.2019.61.1.095

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)
Publication Information
Journal of The Korean Society of Agricultural Engineers / v.61, no.1, 2019 , pp. 95-105 More about this Journal
Abstract
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.
Keywords
Artificial neural network; evapotranspiration; FAO-56 Penman Monteith modthod; Hargreaves method;
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Times Cited By KSCI : 3  (Citation Analysis)
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1 Jesen, M. E. (Ed), 1974. Consumptive use of water and irrigation water requirements. Rep. Tech, Comm. on Irrigation, p. 277.
2 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).
3 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.   DOI
4 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.   DOI
5 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).   DOI
6 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.   DOI
7 Makridakis, S., S. C. Wheelwright, and R. J. Hyndman, 1998. Forecasting-mothods and application (Third Ed.). Wiley, New York, pp. 42-50.
8 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.
9 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.   DOI
10 Oh, S. K., 2008. Pattern recognition. Kyobo Moongo, Seoul, p. 98.
11 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).   DOI
12 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).   DOI
13 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).   DOI
14 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.   DOI
15 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.
16 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.   DOI
17 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.
18 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).   DOI
19 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.
20 APEC Cliamte Center, Clipped CMIP5 data, http://adss.apcc21.org/DataSet/CMIP5/cmip5,jsp. Accessed 31 Mar. 2017.
21 Hargreaves, G. H., 1975. Moisture availability and crop production. Transactions of ASAE 18(5): 980-984. doi:10.13031/2013.36722.   DOI
22 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.   DOI