Comparison of radial basis function networks and empirical equations for converting from pan evaporation to reference evapotranspiration

Hydrological Processes

Volumn 23, Issue 6, 2009, Pages 874-880

  Trajkovic, Slavisa ^a  

^a UNIVERSITY OF NI   (Serbia)

Author keywords

Class A pan; Evaporation; Evapotranspiration; Lysimeters; Neural networks

Indexed keywords

ATTITUDE CONTROL; CONDUCTIVE PLASTICS; EVAPORATION; EVAPOTRANSPIRATION; LYSIMETERS; NEURAL NETWORKS; SOIL SURVEYS; VAPORS; WATER SUPPLY;

CLASS A PAN; EMPIRICAL EQUATIONS; EVAPORATION PANS; HYDROLOGICAL CYCLES; IDAHO , USA; LOW COSTS; PAN EVAPORATIONS; PENMAN-MONTEITH EQUATIONS; RADIAL BASIS FUNCTIONS; RBF NETWORKS; REFERENCE EVAPOTRANSPIRATIONS; SUMMARY STATISTICS; WATER RESOURCES MANAGEMENTS; WEATHER DATUM;

RADIAL BASIS FUNCTION NETWORKS;

ARTIFICIAL NEURAL NETWORK; COMPARATIVE STUDY; DATA PROCESSING; EMPIRICAL ANALYSIS; ESTIMATION METHOD; EVAPORATION; EVAPOTRANSPIRATION; HYDROLOGICAL CYCLE; LYSIMETER; WATER MANAGEMENT;

EID: 62249130774     PISSN: 08856087     EISSN: 10991085     Source Type: Journal    
DOI: 10.1002/hyp.7221     Document Type: Article

References (39)

1. Allen RG. 1996. Assessing integrity of weather data for reference evapotranspiration estimation. Journal of Irrigation and Drainage Engineering 122(2): 97-106.
2. Allen RG, Pruitt WO. 1991. FAO-24 reference evapotranspiration factors. Journal of Irrigation and Drainage Engineering 117(5): 758-773.
3. Allen RG, Jensen ME, Wright ME, Burman RD. 1989. Operational estimates of reference evapotranspiration. Agronomy Journal 81(4): 650-662.
4. Allen RG, Pereira LS, Raes D, Smith M. 1998. Crop evapotranspiration. Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, Roma, Italy.
5. Berengena J, Gavilan P. 2005. Reference evapotranspiration estimation in a highly advective semiarid environment. Journal of Irrigation and Drainage Engineering 131(2): 147-163.
6. Caliandro A, Catalano M, Rubino P, Boari F. 1990. Research on the suitability of some empirical methods for estimating the reference crop evapotranspiration in southern Italy. Paper presented at 1st Congress of the Europian Society of Agronomy, Session 2 P 65.
7. Doorenbos J, Pruitt WO. 1977. Crop Water Requirments. FAO Irrigation and Drainage Paper 24, Roma, Italy.
8. Frevert DK, Hill RW, Braaten BC. 1983. Estimation of FAO evapotranspiration coefficients. Journal of Irrigation and Drainage Engineering 109(2): 265-270.
9. Gavilan P, Berengena J, Allen RG. 2007. Measuring versus estimating net radiation and soil flux: impact on Penman-Monteith reference ET estimates in semiarid regions. Agricultural Water Management 89(3): 275-286.
10. Grismer ME, Orang M, Snyder R, Matyac R. 2002. Pan evaporation to reference evapotranspiration conversion methods. Journal of Irrigation and Drainage Engineering 128(3): 180-184.
11. Gundekar HG, Khodke UM, Sarkar S, Rai RK. 2008. Evaluation of pan coefficient for reference evapotranspation for semi-arid region. Irrigation Science 26(2): 169-175.
12. Irmak S, Haman DZ, Jones JW. 2002. Evaluation of Class A pan coefficients for estimating reference evapotranspiration in humid location. Journal of Irrigation and Drainage Engineering 128(3): 153-159.
13. Irmak S, Allen RG, Whitty EB. 2003. Daily grass and alfalfa-reference - evapotranspiration estimates and alfalfa-to-grass evapo-transpiration ratios in Florida. Journal of Irrigation and Drainage Engineering 129(5): 360-370.
14. Jensen ME, Burman RD, Allen RG. 1990. Evapotranspiration and irrigation water requirements. ASCE manuals and reports on engineering practice No.70, New York.
15. Katul GG, Cuenca R, Grebet HP, Wright JL, Pruitt WO. 1992. Analysis of evaporative flux data for various climates. Journal of Irrigation and Drainage Engineering 118(4): 601-618.
16. Khoob AR. 2008. Comparative study of Hargreaves's and artificial neural network's methodologies in estimating reference evapotranspiration in a semiarid environment. Irrigation Science 26(3): 253-259.
17. Kim S, Kim HS. 2008. Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling. Journal of Hydrology 351(3-4): 299-317.
18. Kisi O. 2006a. Evapotranspiration estimation using feed-forward neural networks. Nordic Hydrology 37(3): 247-260.
19. Kisi O. 2006b. Generalized regression neural networks for evapotranspiration modeling. Hydrological Sciences Journal 51(6): 1092-1105.
20. Kisi O. 2007. Evapotranspiration modeling from climatic data using a neural computing technique. Hydrological Processes 21(14): 1925-1934.
21. Kisi O. 2008. The potential of different ANN techniques in evapotranspiration modeling. Hydrological Processes 22(14): 2449-2460.
22. Kumar M, Raghuwanshi NS, Singh R, Wallender WW, Pruitt WO. 2002. Estimating evapotranspiration using artificial neural network. Journal of Irrigation and Drainage Engineering, 128(4): 224-233.
23. Landeras G, Ortiz-Barredo A, Lo'pez 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). Agricultural Water Management 95(5): 553-565.
24. Lecina S, Martinez-Cob A, Perez PJ, Villalobos FJ, Baselga JJ. 2003. Fixed versus variable bulk canopy resistance for reference evapotranspiration estimation using the Penman-Monteith equation under semiarid conditions. Agricultural Water Management 60(3): 181-198.
25. Lopez-Urrea R, de Santa Olalla FM, Fabeiro C, Moratalla A. 2006. An evaluation of two hourly reference evapotranspiration equations for semiarid conditions. Agricultural Water Management 86(3): 277-282.
26. Martinez-Cob A, Tejero-Juste M. 2004. A wind-based qualitative calibration of the Hargreaves ETo estimation equation in semiarid regions. Agricultural Water Management 64(3): 251-264.
27. Raghuwanshi NS, Wallander WW. 1998. Converting from pan evaporation to evapotranpiration. Journal of Irrigation and Drainage Engineering 124(5): 275-77.
28. Smith M. 1993. CLIMWAT for CROPWAT: a climatic database for irrigation planning and management. FAO Irrigation and Drainage Paper No. 49, Rome, Italy.
29. Snyder RL. 1992. Equation for evaporation pan to evapotranspiration conversions. Journal of Irrigation and Drainage Engineering 118(6): 977-980.
30. Stanhill G. 2002. Is the Class A evaporation pan still the most practical and accurate meteorological method for determining irrigation water requirements? Agricultural and Forest Meteorology, 112(3): 233-236.
31. Sudheer KP, Gosain AK, Ramasastri KS. 2003. Estimating actual evapotranspiration from limited climatic data using neural computing technique. Journal of Irrigation and Drainage Engineering 129(3): 214-218.
32. Todorovic M. 1999. Single-layer evapotranspiration model with variable canopy resistance. Journal of Irrigation and Drainage Engineering 125(5): 235-245.
33. Trajkovic S. 2005. Temperature-Based approaches for estimating reference evapotranspiration. Journal of Irrigation and Drainage Engineering 131(4): 316-323.
34. Trajkovic S, Stankovic M, Todorovic B. 2000a. Estimation of FAO Blaney-Criddle b factor by RBF network. Journal of Irrigation and Drainage Engineering 126(4): 268-271.
35. Trajkovic S, Stojnic V, Todorovic B, Stankovic M. 2000b. Converting from pan evaporation to evapotranspiration using radial basis function networks. Paper presented at 4th International Conference Hydroinformatics, Paper No. 289, 23-27 July, Cedar Rapids, IA, USA.
36. Trajkovic S, Todorovic B, Stankovic M. 2003. Forecasting of reference evapotranspiration by artificial neural networks. Journal of Irrigation and Drainage Engineering 129(6): 454-457.
37. Ventura F, Spano D, Duce P, Snyder RL. 1999. An evaluation of common evapotranspiration equations. Irrigation Science 18(4): 163-170.
38. Wright JL. 1988. Daily and seasonal evapotranspiration and yield of irrigated alfalfa in southern Idaho. Agronomy Journal 80(4): 662-669.
39. Zanetti SS, Sousa EF, Oliveira VPS, Almeida FT, Bernardo S. 2007. Estimating evapotranspiration using artificial neural network and minimum climatological data. Journal of Irrigation and Drainage Engineering 133(2): 83-89.