Introduction - Hydrological Data Driven Modelling - page 14

13. Aussem A, Campbell J, Murtagh F (1998) Wavelet-based feature extraction and
decomposition strategies for financial forecasting. J Comput Intell Finance, 5 − 12
14. Ball RA, Purcell LC, Carey SK et al (2004) Evaluation of solar radiation prediction models in
North America. Agron J 96:391 - 397
15. Berengena J, Gavila ´ in P (2005) Reference ET estimation in a highly advective semi-arid
environment. J Irrig Drain Eng ASCE 131(2):147 163

16. Box GE, Jenkins G (1970) Time Series Analysis, Forecasting and Control, Revised Edition,
Holden-Day, San Francisco. (2nd. Edition, 1976)
17. Bras RL, Rodriguez-Iturbe I (1985) Random functions and hydrology. Addison Wesley,
Massachusetts, pp 559
18. Bray M, Han D (2004) Identi cation of support vector machines for runoff modelling.
J Hydroinf 6(4):265

—

280
19. Bristow K, Campbell G (1984) On the relationship between incoming solar radiation and
daily maximum and minimum temperature. Agric For Meteorol 31:159

−

166

-

20. Chang JH (1968) Climate and agriculture. Aldine Publishing, Chicago
21. Chang LC, Chang FJ, Chiang YM et al (2004) A two-step ahead recurrent neural network for
stream ow forecasting. Hydrol Process 18:81 - 92
22. Chang FJ, Chiang YM, Chang LC et al (2007) Multi-step-ahead neural networks for ood
forecasting. Hydrol Sci J 52(1):114 - 130
23. Dawson CW, Wilby RL (2001) Hydrological modelling using arti cial neural networks.
Prog Phys Geogr 25:80 - 108
24. Dingman SL (1994) Physical hydrology, 1st edn. Prentice Hall, Inc. Simon and Schuster, a
Viacom Company. Upper Saddle River, New Jersey, pp 4

5
25. Donatelli M, Campbell GS (1998) A simple model to estimate global solar radiation.
Proceedings of the Fifth Congress of the European Society for Agronomy. Nitra, Slovakia, II,
pp 133

-

134
26. Donatelli M, Bellocchi G, Fontan F (2003) RadEst3.00: software to estimate daily radiation
data from commonly available meteorological variables. Eur J Agron 18:363

-

367. doi: 10.

−

1016/S1161-0301(02)00130-2
27. Donatelli M, Carlini L, Bellocchi G et al (2006) A software component for estimating
solar radiation. Environ Model Softw 21(3):411 416. doi: 10.1016/j.envsoft.2005.04.002

28. Doorenbos J, Pruitt WO (1977) Guidelines for predicting crop water requirements: Rome,
Italy

-

Food and Agricultural Organization of the United Nations, FAO Irrigation and
Drainage Paper 24, second edn, p 156
29. Duan Q, Gupta VK, Sorooshian S (1993) A shuf ed complex evoluation approach for
effective and ef cient global minimization. J Optim Theory Appl 73(3):501 − 521
30. Duan Q, Sorooshian S, Gupta VK (1994) Optimal use of SCE-UA global optimization
method for calibrating watershed models. J Hydrol 158:265 − 284
31. El-Sha e A, Alsulami HM, Jahanbani H, Najah Ali et al (2013) Multi-lead ahead prediction
model of reference evapotranspiration utilizing ANN with ensemble procedure. Stoch
Environ Res Risk Assess 27:1423

”

1440
32. Encyclopedia of Earth (2009) History of hydrology. http://www.eoearth.org/article/History_
of_hydrology
33. Evans D, Jones AJ (2002) A proof of the gamma test. Proc Roy Soc Ser A458(2027):2759
−

-

2799. http://dx.doi.org/10.1098/rspa.2002
34. Franchini M, Todini E (1981) L ' idrologia sintetica, in Modelli Idrologici ed Idrogeologici,

l'avviamento alla ricerca e perfezionamento, CNR-IRPI, Perugia
35. French MN, Krajewski WF, Cuykendall RR (1992) Rainfall forecasting in space and time
using a neural network. J Hydrol 137:1

Ciclo di seminari per l '

31
36. George W, Pruitt WO, Dong A (1985) Evapotranspiration modelling, in CIMIS Final Report
10013-A In: Snyder, R.L., Pruitt, W.O., Henderson, E.W., Dong, A. (Eds.), U.C. Land, Air,
and Water Research Paper Series B53812, pp 3.36 - 3.61
37. Goh T, Tan K (1977) Stochastic modelling and forecasting of solar radiation data. Sol
Energy 19(6):755 - 757. doi: 10.1016/0038-092X(77)90041-X

-