Estimation of Reference Crop Evapotranspiration Using Backpropagation Neural Network Model
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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)) |
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 |
4 | 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. DOI |
5 | 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. DOI |
6 | 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. |
7 | 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. DOI |
8 | 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. |
9 | 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. DOI |
10 | 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. |
11 | Kecman, V., 2001. Learning and soft computing. London, England: MIT press. |
12 | 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. DOI |
13 | 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. |
14 | Parisi, S., L. Mariani, G. Cola, and T. Maggiore, 2009. Mini-lysimeters evapotranspiration measurements on suburban environment. Italian Journal of Agrometeorologoy 3: 13-16. |
15 | 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. |
16 | 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. DOI |
17 | 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 DOI |
18 | Basheer I. A., and M. Hajmeer, 2000. Artificial neural networks: Fundamentals, computing, design, and application. J. Microbiol. Methods 43(1): 3-31. DOI |
19 | 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. DOI |
20 | 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. DOI |
21 | 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. |
22 | Haykin, S., 1998. Neural networks: A comprehensive foundation. Prentice-Hall, Englewood Cliffs. |
23 | 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. DOI |
24 | 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). DOI |
25 | 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. DOI |
26 | 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. |
27 | 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. DOI |
28 | 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. DOI |
29 | 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. DOI |
30 | 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. DOI |
31 | 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. |
32 | 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. DOI |
33 | 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. DOI |
34 | 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. DOI |
35 | 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. |
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