최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기한국농공학회논문집 = Journal of the Korean Society of Agricultural Engineers, v.61 no.6, 2019년, pp.111 - 121
김민영 (Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA)) , 최용훈 (Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA)) , 수잔 오샤네시 (Conservation and Production Research Laboratory, United States Department of Agriculture, Agricultural Research Service (USDA-ARS)) , 폴 콜레이지 (Conservation and Production Research Laboratory, United States Department of Agriculture, Agricultural Research Service (USDA-ARS)) , 김영진 (Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA)) , 전종길 (Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA)) , 이상봉 (Department of Agricultural Engineering, National Institute of Agricultural Sciences (NAS), Rural Development Administration (RDA))
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 calculat...
* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.
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.
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.
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.
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
Basheer I. A., and M. Hajmeer, 2000. Artificial neural networks: Fundamentals, computing, design, and application. J. Microbiol. Methods 43(1): 3-31.
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.
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.
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.
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.
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.
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.
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.
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.
Haykin, S., 1998. Neural networks: A comprehensive foundation. Prentice-Hall, Englewood Cliffs.
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.
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.
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.
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.
Kecman, V., 2001. Learning and soft computing. London, England: MIT press.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Parisi, S., L. Mariani, G. Cola, and T. Maggiore, 2009. Mini-lysimeters evapotranspiration measurements on suburban environment. Italian Journal of Agrometeorologoy 3: 13-16.
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.
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.
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.
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.
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).
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.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.