Performance Evaluation of ANN and ANFIS Models for Estimating Garlic Crop Evapotranspiration
Publication: Journal of Irrigation and Drainage Engineering
Volume 137, Issue 5
Abstract
Estimation of evapotranspiration (ET) is necessary in water resources management, farm irrigation scheduling, and environmental assessment. Hence, in practical hydrology, it is often necessary to reliably and consistently estimate evapotranspiration. In this study, two artificial intelligence (AI) techniques, including artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), were used to compute garlic crop water requirements. Various architectures and input combinations of the models were compared for modeling garlic crop evapotranspiration. A case study in a semiarid region located in Hamedan Province in Iran was conducted with lysimeter measurements and weather daily data, including maximum temperature, minimum temperature, maximum relative humidity, minimum relative humidity, wind speed, and solar radiation during 2008–2009. Both ANN and ANFIS models produced reasonable results. The ANN, with 6-6-1 architecture, presented a superior ability to estimate garlic crop evapotranspiration. The estimates of the ANN and ANFIS models were compared with the garlic crop evapotranspiration () values measured by lysimeter and those of the crop coefficient approach. Based on these comparisons, it can be concluded that the ANN and ANFIS techniques are suitable for simulation of .
Get full access to this article
View all available purchase options and get full access to this article.
References
Adineh, V. R., Aghanajafi, C., Dehghan, G. H., and Jelvani, S. (2008). “Optimization of the operational parameters in a fast axial flow CW laser using artificial neural networks and genetic algorithms.” Opt. Laser Technol., 40, 1000–1007.
Allen, R. G. (1986). “A Penman for all seasons.” J. Irrig. Drain Eng., 112(4), 348–368.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M. (1998). “Crop evapotranspiration: Guidelines for computing crop water requirements.” FAO Irrigation and Drainage Paper 56, Food and Agriculture Organization of the United Nations, Rome.
ASCE Task Committee. (2000). “Artificial neural networks in hydrology. II: Hydrological applications.” J. Hydrol. Eng., 5(2), 124–137.
Ayars, J. E. (2007). “Water Requirements of Irrigated Garlic.” Proc., American Society of Agricultural and Biological Engineers Int. (ASABE), Paper No. 72285, 2007 ASABE Annual Meeting.
Aytek, A. (2008). “Co-active neurofuzzy inference system for evapotranspiration modeling.” Soft Comput., 13(7), 691–700.
Bazartseren, B., Hildebrandt, G., and Holz, K. (2003). “Short-term water level prediction using neural networks and neuro-fuzzy approach.” Neurocomput. Variable Star Bull., 55, 439–450.
Chauhan, S., and Shrivastava, R. K. (2009). “Performance evaluation of reference evapotranspiration estimation using climate based methods and artificial neural networks.” Water Resour. Manage., 23(5), 825–837.
Clark, G. A., Albergts, E. E., Stanley, C. D., Smajstrala, A. G., and Zazueta, F. S. (1996). “Water requirements and crop coefficients of drip-irrigated strawberry plants.” Trans. ASAE, 39(3), 905–913.
Fabeiro Cortes, C., Martin de Santa Olalla, F., and Lopez-Urea, R. (2003). “Production of garlic (Allium sativum L.) under controlled deficit irrigation in a semi-arid climate.” Agric. Water Manage. 59(2), 155–167.
Food and Agriculture Organization of the United Nations (FAO). (2004). “FAO recommendations on the prevention, control and eradication of highly pathogenic avian influenza (HPAI) in Asia.” FAO Position Paper, Rome, 1–59.
Haman, D. Z., Pritchard, R. T., Smajstrla, A. G., Zazueta, F. S., and Lyrene, P. M. (1997). “Evapotranspiration and crop coefficients for young blueberries in Florida.” Appl. Eng. Agric., 13(2), 209–216.
Hanson, B., May, D., Voss, R., Cantwell, M., and Rice, R. (2003). “Response of garlic to irrigation water.” Agric. Water Manage., 58(1), 29–43.
Irmark, S., Irmark, A., Allen, R. G., and Jones, J. W. (2003). “Solar and net radiation based equations to estimate reference evapotranspiration in humid climates.” J. Irrig. Drain Eng., 129(5), 336–347.
Jang, J. S. R. (1993). “ANFIS: Adaptive network-based fuzzy inference systems.” IEEE Trans. Syst. Man Cybern., 23(3), 665–685.
Keskin, M. E., Terzi, Ö., and Taylan, D. (2009). “Estimating daily pan evaporation using adaptive neural-based fuzzy inference system.” Theor. Appl. Climatol., 98(1–2), 79–87.
Kim, S., and Kim, H. (2008). “Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling.” J. Hydrol. (Amsterdam), 351, 299–317.
Kisi, O. (2005). “Suspended sediment estimation using neuro-fuzzy and neural network approaches.” Hydrol. Sci. J., 50(4), 683–696.
Kisi, O. (2006a). “Evapotranspiration estimation using feed-forward neural network.” Nord. Hydrol., 37(3), 247–260.
Kisi, O. (2006b). “Daily pan evaporation modeling using a neuro-fuzzy computing technique.” J. Hydrol. (Amsterdam), 329, 636–646.
Kisi, O. (2007). “Evapotranspiration modeling from climatic data using a neural computing technique.” Hydrol. Processes, 21(14), 1925–1934.
Kisi, O. (2008). “The potential of different ANN techniques in evapotranspiration modeling.” Hydrol. Processes, 22(14), 2449–2460.
Kumar, M., Bandyopadhyay, A., Raghuwanshi, N. S., and Singh, R. (2008). “Comparative study of conventional and artificial neural network-based estimation models.” Irrig. Sci., 26(6), 531–545.
Kumar, M., Raghuwanshi, N. S., Singh, R., Wallender, W. W., and Pruitt, W. O. (2002). “Estimating evapotranspiration using artificial neural network.” J. Irrig. Drain Eng., 128(4), 224–233.
Lin, C.-H., Chao, C., and Chen, W.-F. (2007). “Estimation regional evapotranspiration by adaptive network-based fuzzy inference system for Dan-Shui basin in Taiwan.” J. Chin. Inst. Eng., 30(6), 1091–1096.
Mehta, R., and Jain, S. K. (2008). “Optimal operation of a multi-purpose reservoir using neuro-fuzzy technique.” Water Resour. Manage., 23(3), 509–529.
Moghaddamnia, A., Ghafari Gousheh, M., Piri, J., Amin, S., and Han, D. (2009). “Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques.” Adv. Water Resour., 32(1), 88.
Rahimi Khoob, A. (2007). “Comparative study of Hargreaves’s and artificial neural network’s methodologies in estimating reference evapotranspiration in a semiarid environment.” Irrig. Sci., 26, 253–259.
Sayed, T., Tavakolie, A., and Razavi, A. (2003). “Comparison of adaptive network based fuzzy inference systems and Bspline neuro-fuzzy mode choice models.” J. Comput. Civ. Eng., 17(2), 123–130.
Sepaskhah, A. R., and Andam, M. (2001). “Crop coefficient of sesame in a semi-arid region of I.R. Iran.” Agric. Water Manage., 49(1), 51–63.
Sudheer, K. P., Gosain, A. K., and Ramasastri, K. S. (2003). “Estimation actual evapotranspiration from limited climatic data using neural computing technique.” J. Irrig. Drain Eng., 129(3), 214–218.
Tzimopoulos, C., Mpallas, L., and Papaevangelou, G. (2008). “Estimation of evapotranspiration using fuzzy systems and comparison with the Blaney-Criddle method.” J. Environ. Sci. Technol., 1(4), 181–186.
Villalobos, F. J., Testi, L., and Rizzalli Orgaz, F. (2004). “Evapotranspiration and crop coefficients of irrigated garlic in a semi-arid climate.” Agric. Water Manage., 64(3), 233–249.
Information & Authors
Information
Published In
Journal of Irrigation and Drainage Engineering
Volume 137 • Issue 5 • May 2011
Pages: 280 - 286
Copyright
© 2011 American Society of Civil Engineers.
History
Received: Jan 1, 2010
Accepted: Sep 27, 2010
Published online: Oct 1, 2010
Published in print: May 1, 2011
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.