عنوان مقاله [English]
The need for optimized exploitation of water resources has been increased due to the limited water resources and the different section´s competition. For this purpose, data of six selected synoptic stations including Mashhad, Shiraz, Tabriz, Kermanshah, Khorramabad and Urmia stations were used. Input variables consist of mean temperature (T), relative humidity (RH), sunshine hour (S) and wind speed at 2 m elevation (U2). The M test method was used to determine the length of test period. Since, both Gama index and Standard Error are closed to the axis at the end of figures, the last five-year results were used to test the models. According to the gamma test results, the best input parameters for Mashhad, Shiraz, Tabriz, Kermanshah, Khorramabad and Urmia are respectively (S, U2, RH), (T, U2, RH, S), (T, U2, RH, S), (T, U2, RH), (T, RH, S), (RH, S) under the combined conditions and in a same way, the lowest gamma are 0.005, -0.01, 0.001, -0.002, 0.008, 0.009. Local Linear Regression (LLR), Dynamic Local Linear Regression (DLLR), ANN (conjugate gradient) and ANN (BFGS) models were used to estimate the annual evapotranspiration. The R, MAE, RMSE, MBE, Jakouvidiez (t) and Sabagh (R2/t) criteria were used to evaluate the proposed models. The results showed that the best performance was obtained for the stations; Mashhad, Kermanshah, Tabriz and Shiraz using the best inputs, so that the correlation coefficients for neural network model conjugate gradient were 0.91, 0.98, 0.96 and 0.97, respectively. The general results showed that the non-parametric methods are able to estimate the annual ET, properly.
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. FAO, Rome, 300(9), D05109.
Ansari, H. (2013). Daily Pan Evaporation Mode lling With ANFIS and NNARX. Iran Agricultural Research, 31.
Corcoran, J. J., Wilson, I. D., & Ware, J. A. (2003). Predicting the geo-temporal variations of crime and disorder. International Journal of Forecasting, 19(4), 623-634.
Doğan, E. (2009). Reference evapotranspiration estimation using adaptive neuro‐fuzzy inference systems. Irrigation and Drainage: The journal of the International Commission on Irrigation and Drainage, 58(5), 617-628.
Durrant, P. J. (2001). winGamma: A non-linear data analysis and modelling tool with applications to flood prediction. Unpublished Ph.D. thesis, Department of Computer Science, Cardiff University, Wales, UK
Entesari, M.R., Norouzi, M., Salamat, A.S., Ehsani, M.R., Tavakoli, A.S. (2007). Comparison Penman - Montith with other recommended methods for calculating potential evapotranspiration (ET0) in several different regions of Iran. eighth roceedings seminar on National Committee of Irrigation and Drainage, Paper No. 11, pages 237-221. (In Persian)
Ghabayiee sough, M., Mosaedi, A., Hussam, M., Hezarjarib, l. (2010). Evaluation of pre processing the input parameters to ANN - Artificial (ANNs) using regression step by step and the Gamma test to estimate a more rapid daily evapotranspiration. Journal of Water and Soil, 24 (3), 610-624. (In Persian).
Jacovides, C. P. (1997). Reply to comment on Statistical procedures for the evaluation of evapotranspiration models. Agricultural water management, 3, 95-97.
Jia Bing, C. (2004). Prediction of daily reference evapotranspiration using adaptive neurofuzzy inference system. Trans of the Chinese society of Agricultural Engineering, 20(4).13-16.
Jones, A. J., Tsui, A., & De Oliveira, A. G. (2002). Neural models of arbitrary chaotic systems: construction and the role of time delayed feedback in control and synchronization. complexity international, 9(2002).
Kişi, Ö., & Öztürk, Ö. (2007). Adaptive neurofuzzy computing technique for evapotranspiration estimation. Journal of Irrigation and Drainage Engineering, 133(4), 368-379.
Kisi, O. (2010). Fuzzy genetic approach for modeling reference evapotranspiration. Journal of irrigation and drainage engineering, 136(3), 175-183.
Koncar, N. (1997). Optimisation strategies for direct inverse neurocontrol (Doctoral dissertation, Imperial College London (University of London)).
Li, Y., Horton, R., Ren, T., & Chen, C. (2010). Prediction of annual reference evapotranspiration using climatic data. Agricultural Water Management, 97(2), 300-308.
Mousavi baygi, M., Erfanian, M., Sarmad, M. 2009. Using at least meteorological data for estimating reference evapotranspiration and provide breeding coefficients (Case Study: Khorasan Razavi province). Journal of soil water (Agricultural Science and Technology), 23(1), 91-99. (In Persian).
Moghaddamnia, A., Gousheh, M. G., Piri, J., Amin, S., & Han, D. (2009b). Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Advances in Water Resources, 32(1), 88-97.
Moghaddamnia, A., Remesan, R., Kashani, M. H., Mohammadi, M., Han, D., & Piri, J. (2009a). Comparison of LLR, MLP, Elman, NNARX and ANFIS Models—with a case study in solar radiation estimation. Journal of Atmospheric and Solar-Terrestrial Physics, 71(8-9), 975-982.
Odhiambo, L. O., Yoder, R. E., & Yoder, D. C. (2001). Estimation of reference crop evapotranspiration using fuzzy state models. Transactions of the ASAE, 44(3), 543.
Odhiambo, L. O., Yoder, R. E., Yoder, D. C., & Hines, J. W. (2001). Optimization of fuzzy evapotranspiration model through neural training with input–output examples. Transactions of the ASAE, 44(6), 1625.
Sabzi parvar, A.A., Taffazoli, F., Zare abiane, H., Banezhad, H., Mosavi bayegi, M., Ghafori, M., Mohseni movahed, A., Mrianji, Z. (2008). Comparison of several models to estimate reference evapotranspiration in a cold and semi arid climates in order to optimize usage of radiation models,The Journal of soil and water (Agricultural Industry and Sciences), 22(2),
Sumner, D. M., & Jacobs, J. M. (2005). Utility of Penman–Monteith, Priestley–Taylor, reference evapotranspiration, and pan evaporation methods to estimate pasture evapotranspiration. Journal of Hydrology, 308(1-4), 81-104.
Shayan neazhad, M., Sadaty nezhad, S. H., Fahmi, H. (2007). Estimation potentialevapot ranspiration with fuzzy regression. Water resource journal, 3(3), 9-19. (In Persian).
Tsui, A.P.M. (1999). Smooth data modelling and stimulus-response via stabilization of neural chaos (Doctoral dissertation, University of London).