Abstract
Accurate estimation of reference crop evapotranspiration (ETo) is required for several hydrological studies and thus, in the past, a number of ETo estimation methods have been developed with different degree of complexity and data requirement. The present study was carried out to develop artificial neural network (ANN) based reference crop evapotranspiration models corresponding to the ASCE’s best ranking conventional ETo estimation methods (Jensen et al. ASCE Manual and Rep. on Engrg. Pract. no. 70, 1990). Among the radiation methods, FAO-24 radiation (or Rad) method for arid and Turc method for humid region, and among the temperature methods, FAO-24 Blaney–Criddle (or BC) method were studied. The ANN architectures corresponding to the above three less data-intensive methods were developed for four CIMIS (California Irrigation Management Information System) stations, namely, Davis, Castroville, Mulberry, and West Side Field station. The comprehensive ANN architecture developed by Kumar et al. (J Irrig Drain Eng 128(4):224–233, 2002) corresponding to Penman–Monteith (PM) ETo for Davis was also tried for the other three stations. Daily meteorological data for a period of more than 10 years (01 January 1990 to 30 June 2000) were collected from these stations and were used to train, test, and validate the ANN models. Two learning schemes, namely, standard back-propagation with learning rate of 0.2 and standard back-propagation with momentum having learning rate of 0.2 and momentum term of 0.95 were considered. ETo estimation performance of the ANN models was compared with the FAO-56 PM method. It was found that the ANN models gave better closeness to FAO-56 PM ETo than the best ranking method in each category (radiation and temperature). Thus these models can be used for ETo estimation in agreement with climatic data availability, when not all required climatic variables are observed.
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Communicated by A. Kassam.
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Kumar, M., Bandyopadhyay, A., Raghuwanshi, N.S. et al. Comparative study of conventional and artificial neural network-based ETo estimation models. Irrig Sci 26, 531–545 (2008). https://doi.org/10.1007/s00271-008-0114-3
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DOI: https://doi.org/10.1007/s00271-008-0114-3