Abstract
Estimation of crop evapotranspiration (ETC) for certain crops such as potato is very important for irrigation planning, irrigation scheduling and irrigation systems management. The primary focus of this study was to investigate the accuracy of the adaptive neurofuzzy inference system (ANFIS) and support vector machines (SVM) for potato ETC estimation when lysimeter measurements or the complete weather data for applying the FAO method are not available. The estimates of the ANFIS and SVM models were compared with the empirical equations of Blaney–Criddle, Makkink, Turc, Priestley–Taylor, Hargreaves and Ritchie. The performances of the different SVM and ANFIS models were evaluated by comparing the corresponding values of root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (r). The drawn conclusions confirmed that the SVM and ANFIS models could provide more accurate ETC estimates than the empirical equations. Overall, the minimum RMSE (0.042 mm/day) and MAE (0.031 mm/day) values and the maximum r value (0.98) were obtained using the SVM model with mean air temperature, relative humidity, solar radiation, sunshine hours and wind speed as inputs.
Similar content being viewed by others
References
Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration. Guidelines for computing crop water requirements. FAO irrigation and drainage. Paper no. 56. FAO, Rome
Aytek A (2008) Co-active neurofuzzy inference system for evapotranspiration Modeling. Soft Comput 13(7):691–700
Blaney HF, Criddle WD (1950) Determining water requirements in irrigated areas from climatological and irrigation data. Soil conservation service technical paper 96, Soil Conservation Service. U.S. Dept. of Agriculture, Washington
Box GEP, Jenkins GM (1976) Time series analysis: forecasting and control. Holden Day Inc., San Francisco
Bray M, Han D (2004) Identification of support vector machines for runoff modeling. J Hydroinform 6(4):265–280
Camps-Valls G, Gomez-Chova L, Calpe-Maravilla J, Martin-Guerrero JD, Soria-Olivas E, Alonso-Chorda L, Moreno J (2004) Robust support vector method for hyperspectral data classification and knowledge discovery. Trans Geosci Rem Sens 42(7):1530–1542
Chauhan S, Shrivastava RK (2008) Performance evaluation of reference evapotranspiration estimation using climate based methods and artificial neural networks. Water Resour Manage. doi:10.1007/s11269-008-9301-5
Cobaner M (2011) Evapotranspiration estimation by two different neuro-fuzzy inference systems. J Hydrol 398(3–4):299–302
Coskun O, Kisi O, Akay B (2011) Neural networks with artificial bee colony algorithm for modeling daily reference evapotranspiration. Irrig Sci 29(6):431–441
De Martonne E (1926) Une nouvelle fonction climatologi-que: L’ indice d’aridite. La Meteorologie 2:449–458
Doorenbos J, Pruitt WO (1977) Crop water requirements. FAO irrigation and drainage. Paper no. 24 (rev.). FAO, Rome
Garcia M, Raes D, Allen R, Herbas C (2004) Dynamics of reference evapotranspiration in the Bolivian highlands (Altiplano). Agric For Meteorol 125:67–82
Gavin H, Agnew CA (2004) Modeling actual, reference, and equilibrium evaporation from a temperate wetland. Hydrol Process 18(2):229–246
Hargreaves GL, Samani ZA (1985) Reference crop evapotranspiration from temperature. Appl Eng Agric 1(2):96–99
Jain SK, Nayak PC, Sudheer KP (2008) Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation. Hydrol Process 22:2225–2234
Jang J (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Transact Syst Man Cybern 23(3):665–685
Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice-Hall, New Jersey
Jones JW, Ritchie JT (1990) Crop growth models management of farm irrigation systems. In: Hoffman GJ, Howel TA, Solomon KH (eds) ASAE monograph no. 9. ASAE, St. Joseph, Mich, pp 63–89
Karimaldini F, Shuib LT, Mohamed TA, Abdollahi M, Khalili N (2011) Daily evapotranspiration modeling from limited weather data using neuro-fuzzy computing technique. J Irrig Drain Engin. doi:10.1061/(ASCE)IR.1943-4774.0000343
Kashyap PS, Panda RK (2001) Evaluation of evapotranspiration estimation methods and development of crop- coefficients for potato crop in a sub-humid region. Agric Water Manag 50:9–25
Kim S, Kim HS (2008) Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling. J Hydrol 351:299–317
Kişi Ö (2006) Generalized regression neural networks for evapotranspiration modeling. Hydrol Sci J 51:1092–1105
Kişi Ö (2007) Evapotranspiration modeling from climatic data using a neural computing technique. Hydrol Process 21(6):1925–1934
Kişi Ö (2008) The potential of different ANN techniques in evapotranspiration modelling. Hydrol Process 22:2449–2460
Kişi Ö, Çimen M (2009) Evapotranspiration modelling using support vector machines. Hydrol Sci J 54(5):918–928
Kişi Ö, Guven A (2009) Evapotranspiration modeling using linear genetic programming technique. J Irrig Drain Eng 136(10):715–723
Kişi Ö, Öztürk Ö (2007) Adaptive neurofuzzy computing technique for evapotranspiration estimation. J Irrig Drain Eng 133(4):368–379
Kumar M, Raghuwanshi NS, Singh R, Wallender WW, Pruitt WO (2002) Estimating evapotranspiration using artificial neural network. J Irrig Drain Eng 128(4):224–233
Kurtulus B, Razack M (2010) Modeling daily discharge responses of a large karstic aquifer using soft computing methods: artificial neural network and neuro-fuzzy. J Hydrol 381:101–111
Landeras G, Ortiz-Barredo A, Lopez JJ (2008) Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain). Agric Water Manag 95:553–565
Lee TS, Najim MMM, Aminul MH (2004) Estimating evapotranspiration of irrigated rice at the West Coast of the Peninsular of Malaysia. J Appl Irrig Sci 39:103–117
Lopez-Urrea R, Martı′n de Santa Olalla F, Fabeiro C, Moratalla A (2006) Testing evapotranspiration equations using lysimeter observations in a semi-arid climate. Agric Water Manag 85:15–26
Makkink GF (1957) Testing the Penman formula by means of lysimeters. J Inst Water Eng 11:277–288
Moghaddamnia A, Ghafari Gousheh M, Piri J, Amin S, Han D (2009) Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Adv Water Resour 32:88–97
Mohandes MA, Halawani TO, Rehman S, Hussain AA (2004) Support vector machines for wind speed prediction. Renew Energ 29:939–947
Ozger M, Yildirım G (2009) Determining turbulent flow friction coefficient using adaptive neuro-fuzzy computing technique. Adv Eng Softw 40:281–287
Popova Z, Kercheva M, Pereira L (2006) Validation of the FAO methodology for computing ET with limited data.application to south Bulgaria. Irrig Drain 55:201–215
Priestley CHB, Taylor RJ (1972) On the assessment of surface heat flux and evapotranspiration using large scale parameters. Mon Weather Rev 100:81–92
Sabziparvar AA, Tabari H (2010) Regional estimation of reference evapotranspiration in arid and semi-arid regions. J Irrig Drain Eng 136(10):724–731
Sabziparvar AA, Tabari H, Aeini A, Ghafouri M (2010) Evaluation of class A pan coefficient models for estimation of reference crop evapotranspiration in cold-semi arid and warm arid climates. Water Resour Manage 24(5):909–920
Sayed T, Tavakolie A, Razavi A (2003) Comparison of adaptive network based fuzzy inference systems and B-spline neuro-fuzzy mode choice models. Water Resourc Res 17(2):123–130
Sentelhas PC, Gillespie TJ, Santos EA (2010) Evaluation of FAO Penman–Monteith and alternative methods for estimating reference evapotranspiration with missing data in Southern Ontario, Canada. Agric Water Manag 97:635–644
Setlak G (2008) The fuzzy-neuro classifier for decision support. Int J Inform Theor Appl 15:21–26
Slim C (2006) Neuro-Fuzzy Network based on Extended Kalman Filtering for Financial Time Series. World Acad Sci Eng Technol 22:134–139
Smith M, Allen R, Pereira L (1997) Revised FAO methodology for crop water requirements. Land and Water Development Division, FAO, Rome
Smrekar J, Assadi M, Fast M, Kustrin I, De S (2009) Development of artificial neural network model for a coal-fired boiler using real plant data. Energy 34:144–152
Sudheer KP, Gosain AK, Ramasastri KS (2003) Estimating actual evapotranspiration from limited climate data using neural computing technique. J Irrg Drain Eng 129(3):214–218
Sugeno M (1985) Industrial applications of fuzzy control. Elsevier, Amsterdam
Tabari H (2010) Evaluation of reference crop evapotranspiration equations in various climates. Water Resourc Manage 24:2311–2337
Tabari H, Hosseinzadeh Talaee P (2011) Local calibration of the Hargreaves and Priestley–Taylor equations for estimating reference evapotranspiration in arid and cold climates of Iran based on the Penman-Monteith model. J Hydrol Eng. doi:10.1061/(ASCE)HE.1943-5584.0000366
Tabari H, Grismer ME, Trajkovic S (2011) Comparative analysis of 31 reference evapotranspiration methods under humid conditions. Irrig Sci. doi:10.1007/s00271-011-0295-z
Trajkovic S (2005) Temperature-based approaches for estimating reference evapotranspiration. J Irrig Drain Eng 131(4):316–323
Trajkovic S, Todorovic B, Stankovic M (2003) Forecasting reference evapotranspiration by artificial neural networks. J. Irrig Drain Eng 129(6):454–457
Traore S, Wang Y-M, Kerh T (2010) Artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian zone. Agric Water Manag 97:707–714
Turc L (1961) Evaluation des besoins en eau irrigation, l’evapotranspiration potentielle. Ann Agron 12:13–49
Vapnik V (1995) The nature of statistical learning theory. Springer, New York
Walter IA et al (2000) ASCE’s standardized reference evapotranspiration equation. In: Proceedings of 4th national irrigation symposium, ASAE, Phoenix, pp 209–215
Zhou H, Li W, Zhang C, Liu J (2009) Ice breakup forecast in the reach of the Yellow River: the support vector machines approach. Hydrol Earth Syst Sci Discuss 6:3175–3198
Acknowledgments
The authors acknowledge the Islamic Republic of Iran Meteorological Organization (IRIMO) for providing the data sets required. Two anonymous reviewers are also acknowledged for their helpful comments, which resulted in an improved paper.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by K. Stone.
Rights and permissions
About this article
Cite this article
Tabari, H., Martinez, C., Ezani, A. et al. Applicability of support vector machines and adaptive neurofuzzy inference system for modeling potato crop evapotranspiration. Irrig Sci 31, 575–588 (2013). https://doi.org/10.1007/s00271-012-0332-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00271-012-0332-6