Performance evaluation of artificial neural networks in estimating reference evapotranspiration with minimal meteorological data

Global Nest Journal

Volumn 13, Issue 1, 2011, Pages 18-27

  Diamantopoulou, M J ^a   Georgiou, P E ^a   Papamichail, D M ^a  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

Author keywords

Calibration; Evapotranspiration; Neural network techniques; Validation

Indexed keywords

EID: 84555189376     PISSN: 11084006     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (46)

1. 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.
2. Allen R.G., Walter I.A., Elliott R.L., Howell T.A., Itenfisu D., Jensen M.E. and Snyder R.L. (2005), The ASCE standardized reference evapotranspiration equation, Task Committee on Standardization of Reference Evapotranspiration of the EWRI of the ASCE, USA.
3. Alexandris S., Kerkides P. and Liakatas A. (2006), Daily reference evapotranspiration estimates by the "Copais" approach, Agricultural Water Management, 82, 371-386.
4. Benvenuto F. and Marani A. (2000), Neural networks for environmental problems: Data quality control and air pollution nowcasting, Global NEST Journal, 2, 281-292.
5. Brown R.G. and Hwang P.Y.C. (1992), Introduction to random signals and applied Kalman filtering, 2nd ed., Wiley, New York.
6. Demuth H. and Beale M. (2001), Neural network toolbox, For use with Matlab®, User's Guide, V4.
7. Diamantopoulou M.J. (2010), Filling gaps in diameter measurements on standing tree boles in the urban forest of Thessaloniki, Greece, Environmental Modelling and Software, 25, 1857-1865.
8. Diamantopoulou M.J., Milios E., Doganos, D. and Bistinas I. (2009), Artificial neural network modeling for reforestation design through the dominant trees bole-volume estimation, Natural Resource Modelling, 22, 511-543.
9. Drago G.P. and Ridella S. (1994), Convergence properties of cascade correlation in Function Approximation, Neural Computing and Applications, 2, 142-147.
10. Droogers P. and Allen R.G. (2002), Estimating reference evapotranspiration under inaccurate data conditions, Irrigation and Drainage Systems, 16, 33-45.
11. Fahlman S.E. and Lebiere C. (1990), The cascade correlation learning architecture. p. 524-532, In: Touretsky, D.S., ed. Advances in Neural Information Processing Systems 2, San Mateo, CA, Morgan Kaufmann.
12. Fausett L. (1994), Fundamentals of neural networks architectures, Algorithms and applications, Prentice Hall, USA.
13. Georgiou P.E. and Papamichail D.M. (2008), Optimization model of an irrigation reservoir for water allocation and crop planning under various weather conditions, Irrigation Science, 26, 487-504.
14. Grewal M.S. and Andrews A.P. (2001), Kalman filtering, Theory and practice using MATLAB®, 2nd ed. Wiley, New York.
15. Hargreaves G.H. and Allen R.G. (2003), History and evaluation of Hargreaves evapotranspiration equation, Journal of Irrigation and Drainage Engineering, 129, 53-63.
16. Hargreaves G.H. and Samani Z.A. (1985), Reference crop evapotranspiration from temperature, Applied Engineering in Agriculture, 1, 96-99.
17. Hargreaves L.G., Hargreaves G.H. and Riley J.R. (1985), Irrigation water requirements for Senegal River Basin, Journal of Irrigation and Drainage Engineering, 111, 265-275.
18. Haykin, S. (1994), Neural networks: A comprehensive foundation, Macmillan, New York.
19. Hoaglin D.C., Mosteller F. and Tukey J.W. (1985), Exploring data tables, trends and shapes, John Wiley and Sons, New York.
20. Karunanithi N., Grenney W.J., Whitley D. and Bovee K. (1994), Neural networks for river flow forecasting, Journal of Computing in Civil Engineering, 8, 201-218.
21. Keskin M.E. and Terzi O. (2006), Artificial neural network models of daily pan evaporation, Journal of Hydrologic Engineering, 11, 65-70.
22. Khoob A.R. (2008), Arificial neural network estimation of reference evapotranspiration from pan evaporation in a semi-arid environment, Irrigation Science, 27, 35-39.
23. Kim S. and Kim H.S. (2008), Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling, Agricultural Water Management, 351, 299-317.
24. Kisi O. (2006), Generalized regression neural networks for evapotranspiration modeling, Hydrological Sciences Journal, 51, 1092-1105.
25. Kisi, O. (2007), Evapotranspiration modeling from climatic data using a neural computing technique, Hydrological Processes, 21, 1925-1934.
26. Kisi O. (2008), The potential of different ANN techniques in evapotranspiration modeling, Hydrological Processes, 22, 2449-2460.
27. o estimation models, Irrigation Science, 26, 531-545.
28. Kumar M., Raghuwanshi N.S. and Singh R. (2009), Development and validation of GANN model for evapotranspiration estimation, Journal of Hydrologic Engineering, 14, 131-140.
29. Kumar M., Raghuwanshi N.S., Singh R., Wallender W.W. and Pruitt W.O. (2002), Estimating evapotranspiration using artificial neural network, Journal of Irrigation and Drainage Engineering, 128, 224-233.
30. Landeras G., Ortiz-Barredo A. and Lopez J.J. (2009), Forecasting weekly evapotranspiration with ARIMA and artificial neural network models, Journal of Irrigation and Drainage Engineering, 135, 323-334.
31. Lekkas D.F., Onof C., Lee M.J. and Baltas E.A. (2004), Application of artificial neural networks for flood forecasting, Global NEST Journal, 6, 205-211.
32. Li S., Wunsch D.C., O'Hair E. and Giesselmann M.G. (2002), Extended Kalman filter training of neural networks on a SIMD Parallel Machine, Journal of Parallel and Distributed Computing, 62, 544-562.
33. Loukas A., Vasiliades L. and Dalezios N. (2002), A hydrologic modeling procedure using a watershed model and artificial neural networks, Proc. of Inter. Conf., Water Resources Management in the ERA of Transition, European Water Resources Association, Athens, 219-228.
34. Maier H.R. and Dandy G.C. (2000), Neural Networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications, Environmental Modelling and Software, 15, 101-124.
35. Norusis M.J. (2000), SPSS for Windows, SPSS Inc. Chicago.
36. Prechelt L. (1997), Investigation of the CasCor family of learning algorithms, Neural Network, 10, 885-896.
37. Reynolds M.R.J. (1984), Estimating the error in model predictions, Forest Science, 30, 454-469.
38. Rivals I. and Personnaz L. (1998), A recursive algorithm based on the extended Kalman filter for the training of feed forward neural models, Neurocomputing, 20, 279-294.
39. Samani Z. (2000), Estimating solar radiation and evapotranspiration using minimum climatological data, Journal of Irrigation and Drainage Engineering, 126, 265-267.
40. Sudheer K.P., Gosain A.K. and Ramasastri K.S. (2003), Estimating actual evapotranspiration from limited climatic data using neural computing technique, Journal of Irrigation and Drainage Engineering, 129, 214-218.
41. Trajkovic S. (2005), Temperature-based approaches for estimating reference evapotranspiration, Journal of Irrigation and Drainage Engineering, 131, 316-323.
42. Trajkovic S. (2007), Hargreaves versus Penman-Monteith under humid conditions, Journal of Irrigation and Drainage Engineering, 133, 38-42.
43. Trajkovic S., Todorovic B. and Stankovic M. (2003), Forecasting of reference evapotranspiration by artificial neural networks, Journal of Irrigation and Drainage Engineering, 129, 454-457.
44. Vanderlinden K., Giráldez J.V. and Meirvenne M.V. (2004), Assessing reference evapotranspiration by the Hargreaves method in Southern Spain, Journal of Irrigation and Drainage Engineering, 130, 184-191.
45. Wang Y.M., Kerh T. and Traore S. (2009), Neural networks approaches for modeling river suspended sediment concentration due to tropical storms. Global NEST Journal, 11, 457-466.
46. Willmott C.J. (1981), On the validation of model, Physical Geography, 2, 184-194.