References

MODELLING PAN EVAPORATION USING GENETIC PROGRAMMING


[1] A. Aytek, Co-active neuro-fuzzy inference system for evapotranspiration modelling, Soft Comput. (2008). Doi: 10. 1007/s00500-008-0342-8

[2] A. Aytek and M. Alp, An application of artificial intelligence for rainfall runoff modelling, J. Earth Syst. Sci. 117(2) (2008), 145-155.

[3] A. Aytek and O. Kisi, A genetic programming approach to suspended sediment modelling, J. Hydrol. 351 (2008), 288-298.

[4] V. Babovic and M. Keijzer, Rainfall runoff modelling based on genetic programming, Nord. Hydrol. 33 (2002), 331-343.

[5] R. D. Burman, Intercontinental comparison of evaporation estimates, J. Irrig. Drain Eng. 102 (1976), 109-118.

[6] Defra, Risk, Performance and Uncertainty in Flood and Coastal Defence: A Review; by P. B. Sayers, B. P. Gouldby, J. D. Simm, I. Meadowcroft and J. Hall, R&D Technical Report FD2302/TR1 (2003).

[7] J. Doorenbos and W. O. Pruitt, Crop water requirements, FAO Irrigation and Drainage Paper, No. 24, FAO, Rome, 1977.

[8] J. P. Drecourt, Application of Neural Networks and Genetic Programming to Rainfall Runoff Modelling, D2K Technical Report 0699-1-1, Danish Hydraulic Institute, Denmark, 1999.

[9] EA, Estimation of Open Air Evaporation, R&D W6-048 (2001).

[10] C. Ferreira, Gene Expression Programming in Problem Solving, Paper presented at the 6th Online World Conference on Soft Computing in Industrial Applications (invited tutorial), (2001a).

[11] C. Ferreira, Gene expression programming: A new adaptive algorithm for solving problems, Complex Sys. 13(2) (2001b), 87-129.

[12] H. Gavin and C. A. Agnew, Modelling actual, reference and equilibrium evaporation from a temperate wet grassland, Hydrolo. Proc. 18 (2004), 229-246.

[13] M. A. Ghorbani, O. Makarynskyy, J. Shiri and D. Makarynska, Genetic programming for sea level predictions in an island environment, Int. J. Ocean Climate Sys. 1(1) (2010), 27-35.

[14] O. Giustolisi, Using GP to determine Chezy resistance coefficient in corrugated channels, J. Hydroinfo. (2004), 157-173.

[15] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, Mass, 1989.

[16] A. Guven, A. Aytek, M. I. Yuce and H. Aksoy, Genetic programming-based empirical model for daily reference evapotranspiration estimation, Clean 36(10-11) (2008), 905-912.

[17] M. E. Jensen (eds), Consumptive Use of Water and Irrigation Water Requirements, ASCE, New York, NY, 1974.

[18] R. Khatibi, Barriers Inherent in Flood Forecasting and their Treatments, In: D. W. Knight and A. Y. Shamseldin (eds), River Basin Management for Flood Risk Mitigation (2006).

[19] O. Kisi, Daily pan evaporation modelling using a neuro-fuzzy computing technique, J. Hydrol. 329 (2006a), 636-646.

[20] O. Kisi, Generalized regression neural networks for evapotranspiration modelling, Hydrol. Sci. J. 51(6) (2006b), 1092-1105.

[21] O. Kisi, Evapotranspiration estimation using feed forward neural networks, Nord. Hydrol. 37(3) (2006c), 247-260.

[22] O. Kisi, Evapotranspiration modelling from climate data using a neural computing technique, Hydrol. Proc. 21(6) (2007), 1925-1934.

[23] J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, The MIT Press, Cambridge, MA, 1992.

[24] M. Kumar, N. S. Raghuwanshi, R. Singh, W. W. Wallender and W. O. Pruitt, Estimating evapotranspiration using artificial neural networks, J. Irrig. Drain Eng. 128(4) (2002), 224-233.

[25] D. R. Legates and G. J. McCabe, Evaluating the use of “goodness - of - fit” measures in hydrologic and hydroclimatic validation, Water Resour. Res. 35(1) (1999), 233-241.

[26] E. T. Linarce, Climate and evaporation from crops, J. Irrig. Drain Eng. 93 (1967), 61-79.

[27] S. Y. Liong, T. R. Gautam, S. T. Khu, V. Babovic, M. Keijzer and N. Muttil, Genetic programming: A new paradigm in rainfall runoff modelling, J. Amer. Water Resour. Assoc. 38(3) (2002), 705-718.

[28] A. Moghaddamnia, M. Ghafari Gousheh, J. Piri, S. Amin and D. Han, Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques, Adv. Water Resour. 32 (2009), 88-97. DOI 10.1016/j.advwaters. 2008. 10.005

[29] N. Muttil and S. Y. Liong, Improving Runoff Forecasting by Input Variable Selection in GP, Paper presented at the Proceedings of World Water Congress, ASCE, 2001.

[30] R. Poli and N. F. McPhee, Covariant Parsimony Pressure for Genetic Programming, Technical Report CES-480, ISSN: 1744-8050, (2008).

[31] A. M. A. Salih and U. Sendil, Evapotranspiration under extremely arid climates, J. Irrig. Drain Eng. 110(3) (1984), 289-303.

[32] A. D. Savic, A. G. Walters and J. W. Davidson, A genetic programming approach to rainfall runoff modelling, Water Resour. Manage 13 (1999), 219-231.

[33] A. F. Sheta and A. Mahmoud, Forecasting using Genetic Programming, Paper presented at the 33-rd Southeastern Symposium on System Theory, (2001), 343-347.

[34] J. C. Stephen and E. H. Stewart, A comparison of procedures for computing evaporation and evapotranspiration, Publication 62, International Association of Scientific Hydrology, International Union of Geodynamics and Geophysics, Berkeley, CA, (1963), 123-133.

[35] K. P. Sudheer, A. K. Goasin and K. S. Ramasastri, Estimating actual evapotranspiration from limited climate data using neural computing technique, J. Irrig. Drain Eng. 129(3) (2003), 214-218.

[36] O. Terzi and M. E. Keskin, Evaporation estimation using gene expression programming, J. Appl. Sci. 5(3) (2005), 508-512.

[37] S. Trajkovic, Temperature-based approaches for estimating reference evapotranspiration, J. Irrig. Drain Eng. 131(4) (2005), 316-323.