Estimation of daily reference evapotranspiration by neuro computing techniques using limited data in a semi-arid environment

References

• Allen RG. 2000. Using the FAO-56 dual crop coefficient method over an irrigated region as part of an evapotranspiration intercomparison study. J Hydrol. 229:27–41.
   Web of Science ®Google Scholar
• Allen RG, Pereira LS, Raes D, Smith M. 1998. Crop evapotranspiration: guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Rome: FAO.
   Google Scholar
• Amatya D, Skaggs R, Gregory J. 1995. Comparison of methods for estimating REF-ET. J Irrig Drain Eng. 121:427–435.
   Web of Science ®Google Scholar
• Cığızoğlu H. 2003. Estimation, forecasting and extrapolation of flow data by artificial neural networks. Hydrol Sci J. 48:349–361.
   Web of Science ®Google Scholar
• Cios KJ, Pedrycz W, Swiniarski RW. 1998. Data mining methods for knowledge discovery: data mining and knowledge discovery. New York (NY): Springer.
   Google Scholar
• Daneshmand H, Tavousi T, Khosravi M, Tavakoli S. 2015. Modeling minimum temperature using adaptive neuro-fuzzy inference system based on spectral analysis of climate indices: A case study in Iran. J Saudi Soc Agric Sci. 14:33–40.
   Google Scholar
• Gao X, Peng S, Xu J, Yang S, Wang W. 2015. Proper methods and its calibration for estimating reference evapotranspiration using limited climatic data in Southwestern China. Arch Agron Soil Sci. 61:415–426.
   Web of Science ®Google Scholar
• Gardner MW, Dorling SR. 1998. Artificial neural networks (the multilayer perceptron) - A review of applications in the atmospheric sciences. Atmos Environ. 32:2627–2636.
   Web of Science ®Google Scholar
• Hargreaves GH, Samani ZA. 1985. Reference crop evapotranspiration from temperature. Appl Eng Agric. 1:96–99.
   Google Scholar
• Haykin SS. 2001. Neural networks: a comprehensive foundation. Beijing: Tsinghua University Press.
   Google Scholar
• Jayalakshmi T, Santhakumaran A. 2011. Statistical normalization and back propagationfor classification. Int Comput Theory Eng. 3:89–93.
   Google Scholar
• Jones J, Ritchie J. 1990. Crop growth models. In: Hoffman GJ, Howel TA, Solomon KH, editors. Management of farm irrigation systems. St. Joseph: ASAE; p. 63–89.
   Google Scholar
• Karimaldini F, Teang Shui L, Ahmed Mohamed T, Abdollahi M, Khalili N. 2011. Daily evapotranspiration modeling from limited weather data by using neuro-fuzzy computing technique. J Irrig Drain Eng. 138:21–34.
   Web of Science ®Google Scholar
• Karunanithi N, Grenney WJ, Whitley D, Bovee K. 1994. Neural networks for river flow prediction. J Comput Civ Eng. 8:201–220.
   Web of Science ®Google Scholar
• Khoob AR. 2008a. Artificial neural network estimation of reference evapotranspiration from pan evaporation in a semi-arid environment. Irrig Sci. 27:35–39.
   Web of Science ®Google Scholar
• Khoob AR. 2008b. Comparative study of Hargreaves’s and artificial neural network’s methodologies in estimating reference evapotranspiration in a semiarid environment. Irrig Sci. 26:253–259.
   Web of Science ®Google Scholar
• Kisi O. 2006. Generalized regression neural networks for evapotranspiration modelling. Hydro Sci. 51:1092–1105.
   Web of Science ®Google Scholar
• Kisi O. 2007. Evapotranspiration modelling from climatic data using a neural computing technique. Hydrol Process. 21:1925–1934.
   Web of Science ®Google Scholar
• Kisi O. 2009. Daily pan evaporation modelling using multi‐layer perceptrons and radial basis neural networks. Hydrol Process. 23:213–223.
   Web of Science ®Google Scholar
• Kisi O, Ozturk O. 2007. Adaptive neurofuzzy computing technique for evapotranspiration estimation. J Irrig Drain Eng -Asce. 133:368–379.
   Web of Science ®Google Scholar
• Kumar M, Raghuwanshi N, Singh R. 2011. Artificial neural networks approach in evapotranspiration modeling: a review. Irrig Sci. 29:11–25.
   Web of Science ®Google Scholar
• Kumar M, Raghuwanshi NS, Singh R, Wallender WW, Pruitt WO. 2002. Estimating evapotranspiration using artificial neural network. J Irrig Drain Eng -Asce. 128:224–233.
   Web of Science ®Google Scholar
• Laaboudi A, Mouhouche B, Draoui B. 2012. Neural network approach to reference evapotranspiration modeling from limited climatic data in arid regions. Int J Biometeorol. 56:831–841.
   PubMed Web of Science ®Google Scholar
• Lachtermacher G, Fuller JD. 1995. Back propagation in time‐series forecasting. Int J Forecasting. 14:381–393.
   Google Scholar
• Landeras G, Ortiz-Barredo A, López 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 Manage. 95:553–565.
   Web of Science ®Google Scholar
• Lapedes AS, Farber RM 1988. How neural nets work. In: Anderson DZ, editor. Neural information processing systems-proceedings of a conference held in Denver, Colorado; Nov 1998; New York (NY). AIP-Press, p. 63–89.
   Google Scholar
• Makkink G. 1957. Testing the Penman formula by means of lysimeters. J Inst Water Eng. 11:277–288.
   Google Scholar
• Priddy KL, Keller PE. 2005. Artificial neural networks: an introduction. Washington: SPIE press.
   Google Scholar
• Priestley C, Taylor RJ. 1972. On the assessment of surface heat flux and evaporation using large scale parameters. Mon Weather Rev. 100:81–92.
   Web of Science ®Google Scholar
• Rao V, Rao H. 1995. C++ neural networks and fuzzy logic. MIS: Press.
   Google Scholar
• Shiri J, Sadraddini AA, Nazemi AH, Marti P, Fard AF, Kisi O, Landeras G. 2015. Independent testing for assessing the calibration of the Hargreaves–samani equation: new heuristic alternatives for Iran. Comput Electron Agric. 117:70–80.
   Web of Science ®Google Scholar
• Smith M. 2000. The application of climatic data for planning and management of sustainable rainfed and irrigated crop production. Agric For Meteorol. 103:99–108.
   Web of Science ®Google Scholar
• Srinivasan D, Liew A, Chang C. 1994. A neural network short-term load forecaster. Electr Power Syst Res. 28:227–234.
   Web of Science ®Google Scholar
• Srinivasan R, Ramanarayanan TS, Arnold JG, Bednarz ST. 1998. Large area hydrologic modeling and assessment part II: model application. J Am Water Resour Assoc. 34:91–101.
   Web of Science ®Google Scholar
• Sudheer KP, Gosain AK, Ramasastri KS. 2003. Estimating actual evapotranspiration from limited climatic data using neural computing technique. J Irrig Drain Eng -Asce. 129:214–218.
   Web of Science ®Google Scholar
• Tabari H. 2010. Evaluation of reference crop evapotranspiration equations in various climates. Water Resour Manage. 24:2311–2337.
   Web of Science ®Google Scholar
• Tabari H, Talaee PH. 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. 16:837–845.
   Web of Science ®Google Scholar
• Tabari H, Talaee PH. 2013. Multilayer perceptron for reference evapotranspiration estimation in a semiarid region. Neural Comput Appl. 23:341–348.
   Web of Science ®Google Scholar
• Trajkovic S. 2005. Temperature-based approaches for estimating reference evapotranspiration. J Irrig Drain Eng. 131:316–323.
   Web of Science ®Google Scholar
• Trajkovic S, Todorovic B, Stankovic M. 2003. Forecasting of reference evapotranspiration by artificial neural networks. J Irrig Drain Eng. 129:454–457.
   Web of Science ®Google Scholar
• Traore S, Wang Y-M, Kerh T. 2010. Artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian zone. Agric Water Manage. 97:707–714.
   Web of Science ®Google Scholar
• Turc L. 1961. Estimation of irrigation water requirements, potential evapotranspiration: a simple climatic formula evolved up to date. Ann Agron. 12:13–49.
   Google Scholar
• Valiantzas JD. 2013. Simplified forms for the standardized FAO-56 Penman–monteith reference evapotranspiration using limited weather data. J Hydrol. 505:13–23.
   Web of Science ®Google Scholar
• Weigend AS, Huberman BA, Rumelhart DE 1991. Predicting sunspots and currency rates with connectionist networks. In: Casdagli M, Eubank S, editors. Proceedings of the 1990 NATO workshop on Nonlinear Modeling and Forecasting; Sep 24-28; Santa Fe, NM, USA: Addison-Wesley. p. 395–432.
   Google Scholar
• Zanetti S, Sousa E, Oliveira V, Almeida F, Bernardo S. 2007. Estimating evapotranspiration using artificial neural network and minimum climatological data. J Irrig Drain Eng. 133:83–89.
   Web of Science ®Google Scholar