Application of artificial intelligence to estimate daily pan evaporation using available and estimated climatic Data in the Khozestan Province (South Western Iran)

Journal of Irrigation and Drainage Engineering

Volumn 137, Issue 7, 2011, Pages 412-425

  Shiri, Jalal ^a   Kişi, Özgur ^b  

^a UNIVERSITY OF TABRIZ   (Iran)

^b ERCIYES UNIVERSITY   (Turkey)

Author keywords

Artificial intelligence; Computer programming; Data processing; Evaporation; Fuzzy sets; Iran; Neural networks

Indexed keywords

AIR TEMPERATURE; ARTIFICIAL NEURAL NETWORK; CLIMATIC DATA; CLIMATIC VARIABLES; CORRELATION COEFFICIENT; DAILY PAN EVAPORATION; DATA SETS; DYNAMIC MODELING; EVAPORATION ESTIMATION; EVAPORATION PROCESS; HYDROLOGIC CYCLES; INPUT VECTOR; IRAN; MODEL APPROACH; NEURO-FUZZY; RESIDUAL MASS; ROOT MEAN SQUARE ERRORS; SCATTER INDEX; SOLAR RADIATION DATA; SUNSHINE HOUR; WATER RESOURCES DEVELOPMENT; WIND SPEED;

ARTIFICIAL INTELLIGENCE; DATA HANDLING; ERROR STATISTICS; ESTIMATION; EVAPORATION; FUZZY NEURAL NETWORKS; FUZZY SETS; GENETIC PROGRAMMING; INFORMATION MANAGEMENT; RATING; SOLAR RADIATION; SUN; TIME SERIES; WATER RESOURCES;

PHASE TRANSITIONS;

ARTIFICIAL NEURAL NETWORK; DATA PROCESSING; DATA SET; ESTIMATION METHOD; EVAPORATION; FUZZY MATHEMATICS; GENETIC ALGORITHM; HYDROLOGICAL CYCLE; SOLAR RADIATION;

EID: 79960111650     PISSN: 07339437     EISSN: None     Source Type: Journal    
DOI: 10.1061/(ASCE)IR.1943-4774.0000315     Document Type: Article

References (81)

1. Allen R.G. Pereira L.S. Raes D. Smith M. Crop evapotranspiration-Guidelines for computing crop evapotranspiration. 1998, and Irrigation and Drainage Paper No. 56, FAO, Rome.
2. Artificial neural networks in hydrology. II: Hydrological applications. J. Hydrol. Eng. 2000, 5(2):124-137. ASCE, JHYEFF, 1084-0699, 10.1061/(ASCE)1084-0699(2000)5:2(124).
3. Aytek A. Co-active neurofuzzy inference system for evapotranspiration modeling. Soft Comput. 2008, 13(7):691-700. 1432-7643, 10.1007/s00500-008-0342-8.
4. Aytek A. Alp M. An application of artificial intelligence for rainfall runoff modeling. J. Earth Syst. Sci. 2008, 117(2):145-155. JESSB2, 0253-4126, 10.1007/s12040-008-0005-2, and .
5. Aytek A. Kisi O. genetic programming approach to suspended sediment modeling. J. Hydrol. (Amsterdam) 2008, 351(3-4):288-298. JHYDA7, 0022-1694, 10.1016/j.jhydrol.2007.12.005, and .
6. Babovic V. Kanizares R. Jenson H.R. Klinting A. Neural networks as routine for error updating of numerical models. J. Hydrol. Eng. 2001, 127(3):181-193. JHYEFF, 1084-0699, 10.1061/(ASCE)0733-9429(2001)127:3(181), and .
7. Babovic V. Keijzer M. Rainfall runoff modelling based on genetic programming. Nord. Hydrol. 2002, 33(5):331-343. NOHYBB, 0029-1277, and .
8. Bandyopadhyay A. Bhadra A. Raghuwanshi N.S. Singh R. Estimation of monthly solar radiation from measured air temperature extremes. Agric. For. Meteorol. 2008, 148(11):1707-1718. AFMEEB, 0168-1923, 10.1016/j.agrformet.2008.06.002, and .
9. Banzhaf W. Nordin P. Keller P.E. Francone F.D. Genetic programming 1998, 512. and Morgan Kaufmann, San Francisco.
10. Bruton J.M. McClendon R.W. Hoogenboom G. Estimating daily pan evaporation with artificial neural networks. Trans. ASAE 2000, 43(2):491-496. TAAEAJ, 0001-2351, and .
11. Brutsaert W.H. Evaporation into the atmosphere. 1982, D. Reidel, Dordrecht, Netherlands.
12. Burman R.D. Intercontinental comparison of evaporation estimates. J. Irrig. Drain Eng. 1976, 102(1):109-118. JRCEA4, 0044-7978.
13. Cancelliere A. Giusiano G. Ancarani A. Rossi G. neural networks for deriving irrigation reservoir operating rules. Water Resour. Manage. 2002, 16(1):71-88. WRMAEJ, 0920-4741, 10.1023/A:1015563820136, and .
14. Christiansen J.E. Estimating pan evaporation and evapotranspiration from climatic data. Irrigation and Drainage Specific Conf. 1966, 193-231. ASCE, Reston, VA.
15. Clayton L.H. Prediction of Class pan evaporation in Southwest Idaho. J. Irrig. Drain Eng. 1989, 115(2):166-171. JIDEDH, 0733-9437, 10.1061/(ASCE)0733-9437(1989)115:2(166).
16. Coulomb C.V. Legesse D. Gasse F. Travi Y. Chernet T. Lake evaporation estimates in tropical Africa (Lake Ziway, Ethiopia). J. Hydrol. (Amsterdam) 2001, 245(1-4):1-18. JHYDA7, 0022-1694, 10.1016/S0022-1694(01)00341-9, and .
17. Cybenco G. Approximation by superposition of sigmoidal function. Math. Control Signals Syst. 1989, 2(4):303-314. MCSYE8, 0932-4194, 10.1007/BF02551274.
18. Doorenbos J. Pruitt W.O. Crop water requirements. 1977, and Irrigation and Drainage Paper No. 24, FAO, Rome.
19. Drecourt J.P. Application of neural networks and genetic programming to rainfall runoff modeling. 1999, D2K Technical Rep. 0699-1-1, Danish Hydraulic Institute, Horsholm, Denmark.
20. Feddes R.A. Lenselink K.J. Evapotranspiration. Drainage principles and applications. 1994, 145-173. H. P. Ritzema, and 2nd Ed., ed., Int. Institute for Land Reclamation and Improvement (ILRI), Wageningen, Netherlands.
21. Ferreira C. Gene expression programming in problem solving. 6th Online World Conf. on Soft Computing in Industrial Applications 2001, (a). .
22. Ferreira C. Gene expression programming: A new adaptive algorithm for solving problems. Complex Syst. 2001, 13(2):87-129. CPSYEN, 0891-2513, (b). .
23. Ferreira C. Gene expression programming: Mathematical modeling by an artificial intelligence 2006, 478. Springer, Berlin, Heidelberg, New York.
24. Frevert D.K. Hill R.W. Braaten B.C. Estimation of FAO evapotranspiration coefficients. J. Irrig. Drain Eng. 1983, 109(2):265-270. JIDEDH, 0733-9437, 10.1061/(ASCE)0733-9437(1983)109:2(265), and .
25. Gavin H. Agnew C.A. Modeling actual, reference and equilibrium evaporation from temperate wet grassland. Hydrol. Processes 2004, 18(2):229-246. HYPRE3, 1099-1085, 10.1002/hyp.1372, and .
26. 2010, GeneXpro [Computer software].
27. Ghorbani M.A. Khatibi R. Aytek A. Makarynskyy O. Shiri J. Sea water level forecasting using genetic programming and comparing the performance with artificial neural networks. Comput. Geosci. 2010, 36(5):620-627. CGEODT, 0098-3004, 10.1016/j.cageo.2009.09.014, and .
28. Giustolisi O. Using GP to determine Chezzy resistance coefficient in corrugated channels. J. Hydroinf. 2004, 6(3):157-173. 1464-7141.
29. Goldberg D.E. Genetic algorithms in search, optimization, and machine learning 1989, 432. Addison-Wesley, Reading, MA.
30. Guven A. Aytek A. Yuce M.I. Aksoy H. Genetic programming based empirical model for daily reference evapotranspiration estimation. Clean: Soil, Air, Water 2008, 36(10-11):905-912. CSAWAC, 1863-0650, 10.1002/clen.200800009, and .
31. Guven A. Gunal M. Genetic programming approach for prediction of local scour downstream hydraulic structures. J. Irrig. Drain Eng. 2008, 134(2):241-249. JIDEDH, 0733-9437, 10.1061/(ASCE)0733-9437(2008)134:2(241), and .
32. Hagan M.T. Menhaj M.B. Training feedforward networks with the Marquaradt algorithm. IEEE Trans. Neural Netw. 1994, 5(6):989-993. ITNNEP, 1045-9227, 10.1109/72.329697, and .
33. Hargreaves G.L. Samani Z.A. Estimating potential evapotranspiration. J. Irrig. Drain Eng. 1982, 108(3):225-230. JIDEDH, 0733-9437, and .
34. Harris E.L. Babovic V. Falconer R.A. Velocity predictions in compound channels with vegetated flood plains using genetic programming. Int. J. River Basin Manage. 2003, 1(2):117-123. 1571-5124, 10.1080/15715124.2003.9635198, and .
35. Haykin S. Neural networks-A comprehensive foundation 1998, 26-32. 2nd Ed., Prentice-Hall, Upper Saddle River, NJ.
36. Jackson R.D. Evaluating evapotranspiration at local and regional scales. Proc. IEEE 1985, 73(6):1086-1096. IEEPAD, 0018-9219, 10.1109/PROC.1985.13239.
37. Jain S.K. Singh V.P. van Genuchten M. Analysis of soil water retention data using artificial neural networks. J. Hydrol. Eng. 2004, 9(5):415. JHYEFF, 1084-0699, 10.1061/(ASCE)1084-0699(2004)9:5(415), and .
38. Jang J.S. R. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 1993, 23(3):665-685. ISYMAW, 0018-9472, 10.1109/21.256541.
39. Jang J.S. R. Sun C.T. Neuro fuzzy modeling and control. Proc. IEEE 1995, 83(3):378-405. IEEPAD, 0018-9219, 10.1109/5.364486, and .
40. Jang J.S. R. Sun C.T. Mizutani E. Neurofuzzy and soft computing: computational approach to learning and machine intelligence 1997, and Prentice-Hall, Upper Saddle River, NJ.
41. Jensen M.E. Consumptive use of water and irrigation water requirements 1974, ed. ASCE, New York.
42. Kay A.L. Davies H.N. Calculating potential evaporation from climate model data: source of uncertainty for hydrological climate change impacts. J. Hydrol. (Amsterdam) 2008, 358(3-4):221-239. JHYDA7, 0022-1694, 10.1016/j.jhydrol.2008.06.005, and .
43. Keskin M.E. Terzi O. Artificial neural network models of daily pan evaporation. J. Hydrol. Eng. 2006, 11(1):65-70. JHYEFF, 1084-0699, 10.1061/(ASCE)1084-0699(2006)11:1(65), and .
44. Keskin M.E. Terzi O. Taylan D. Fuzzy logic model approaches to daily pan evaporation estimation in Western Turkey. Hydrol. Sci. J. 2004, 49(6):1001-1010. HSJODN, 0262-6667, 10.1623/hysj.49.6.1001.55718, and .
45. Khu S.T. Liong S.Y. Babovic V. Madsen H. Muttil N. Genetic programming and its application in real-time runoff forming. J. Am. Water Resour. Assoc. 2001, 37(2):439-451. JWRAF5, 1093-474X, 10.1111/j.1752-1688.2001.tb00980.x, and .
46. Kisi O. River flow modeling using artificial neural networks. J. Hydrol. Eng. 2004, 9(1):60-63. JHYEFF, 1084-0699, 10.1061/(ASCE)1084-0699(2004)9:1(60), (a). .
47. Kisi O. Multi-layer perceptions with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation. Hydrol. Sci. J. 2004, 49(6):1025-1040. HSJODN, 0262-6667, 10.1623/hysj.49.6.1025.55720, (b). .
48. Kisi O. Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrol. Sci. J. 2005, 50(4):683-696. HSJODN, 0262-6667.
49. Kisi O. Daily pan evaporation modeling using a neuro-fuzzy computing technique. J. Hydrol. (Amsterdam) 2006, 329(3-4):636-646. JHYDA7, 0022-1694, 10.1016/j.jhydrol.2006.03.015, (a). .
50. Kisi O. Generalized regression neural networks for evapotranspiration modeling. Hydrol. Sci. J. 2006, 51(6):1092-1105. HSJODN, 0262-6667, 10.1623/hysj.51.6.1092, (b). .
51. Kisi O. Evapotranspiration estimation using feed forward neural networks. Nord. Hydrol. 2006, 37(3):247-260. NOHYBB, 0029-1277, 10.2166/nh.2006.010, (c). .
52. Kisi O. Evapotranspiration modeling from climate data using neural computing technique. Hydrol. Processes 2007, 21(14):1925-1934. HYPRE3, 1099-1085, 10.1002/hyp.6403.
53. Kisi O. Neural networks and wavelet conjunction model for intermittent stream flow forecasting. J. Hydrol. Eng. 2009, 14(8):773-782. JHYEFF, 1084-0699, 10.1061/(ASCE)HE.1943-5584.0000053.
54. Kisi O. Ozturk O. Adaptive neurofuzzy computing technique for evapotranspiration estimation. J. Irrig. Drain Eng. 2007, 133(4):368-379. JIDEDH, 0733-9437, 10.1061/(ASCE)0733-9437(2007)133:4(368), and .
55. Koza J.R. Genetic programming: On the programming of computers by means of natural selection 1992, 840. MIT, Cambridge, MA.
56. o estimation models. Irrig. Sci. 2008, 26(6):531-545. IRSCD2, 0342-7188, 10.1007/s00271-008-0114-3, and .
57. Kumar M. Raghuwanshi N.S. Singh R. Wallender W.W. Pruitt W.O. Estimating evapotranspiration using artificial neural networks. J. Irrig. Drain Eng. 2002, 128(4):224-233. JIDEDH, 0733-9437, 10.1061/(ASCE)0733-9437(2002)128:4(224), and .
58. Landeras G. Ortiz-Barredo A. Lopez J.J. Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain). Agric. Water Manage. 2008, 95(5):553-565. AWMADF, 0378-3774, 10.1016/j.agwat.2007.12.011, and .
59. Legates D.R. McCabe G.J. Evaluating the use of goodness-of-fit measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 1999, 35(1):233-241. WRERAQ, 0043-1397, 10.1029/1998WR900018, and .
60. Linarce E.T. Climate and evaporation from crops. J. Irrig. Drain Eng. 1967, 93(4):61-79. JRCEA4, 0044-7978.
61. Maier H.R. Dandy G.C. Neural networks for the prediction and forecasting of water resources variables: review of modeling issues and applications. Environ. Model. Software 2000, 15(1):101-124. EMSOFT, 1364-8152, 10.1016/S1364-8152(99)00007-9, and .
62. Mamdani E.H. Assilian S. An experiment in linguistic synthesis with fuzzy logic controller. Int. J. Man-Mach. Stud. 1975, 7(1):1-13. IJMMBC, 0020-7373, 10.1016/S0020-7373(75)80002-2, and .
63. 2010, MATLAB 7.0 [Computer software]. The Mathworks, Inc., Natick, MA.
64. Minnes A.W. Hall M.J. Artificial neural networks as rainfall-runoff models. Hydrol. Sci. J. 1996, 41(3):399-418. HSJODN, 0262-6667, 10.1080/02626669609491511, and .
65. Moghaddamnia A. Ghafari Gousheh M. Piri J. Amin S. Han D. Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Adv. Water Resour. 2009, 32(1):88-97. AWREDI, 0309-1708, 10.1016/j.advwatres.2008.10.005, and .
66. Odhiambo L.O. Yoder R.E. Yoder D.C. Hines J.W. Optimization of fuzzy evapotranspiration model through neural training with input-output examples. Trans. ASAE 2001, 44(6):1625-1633. TAAEAJ, 0001-2351, and .
67. Preis A. Otsfeld A. coupled model tree-genetic algorithm scheme of flow and water quality predictions in watersheds. J. Hydrol. (Amsterdam) 2008, 349(3-4):364-375. JHYDA7, 0022-1694, 10.1016/j.jhydrol.2007.11.013, and .
68. Reis R.J. Dias N.L. Multi-season lake evaporation: Energy-budget estimates and CRLE model assessment with limited meteorological observations. J. Hydrol. (Amsterdam) 1998, 208(3-4):135-147. JHYDA7, 0022-1694, 10.1016/S0022-1694(98)00160-7, and .
69. Ridder N.A. Boonstra J. Analysis of water balances. Drainage principles and applications 1994, 601-633. H. P. Ritzema, and 2nd Ed., ed., Int. Institute for Land Reclamation and Improvement (ILRI), Wageningen, Netherlands.
70. Russel S.O. Campbell P.F. Reservoir operating rules with fuzzy programming. J. Water Resour. Plann. Manage. 1996, 122(3):165-170. JWRMD5, 0733-9496, 10.1061/(ASCE)0733-9496(1996)122:3(165), and .
71. Salih A.M. A. Sendil U. Evapotranspiration under extremely arid climates. J. Irrig. Drain Eng. 1984, 110(3):289-303. JRCEA4, 0044-7978, 10.1061/(ASCE)0733-9437(1984)110:3(289), and .
72. Savic A.D. Walters A.G. Davidson J.W. genetic programming approach to rainfall-runoff modeling. Water Resour. Manage. 1999, 13(3):219-231. WRMAEJ, 0920-4741, 10.1023/A:1008132509589, and .
73. Stephen J.C. Stewart E.H. comparison of procedures for computing evaporation and evapotranspiration. 1963, 123-133. and Publication 62, Int. Association of Scientific Hydrology, Int. Union of Geodynamics and Geophysics, Berkeley, CA.
74. Sudheer K.P. Goasin A.K. Ramasastri K.S. Estimating actual evapotranspiration from limited climate data using neural computing technique. J. Irrig. Drain Eng. 2003, 129(3):214-218. JRCEA4, 0044-7978, 10.1061/(ASCE)0733-9437(2003)129:3(214), and .
75. Sudheer K.P. Gosain A.K. Rangan D.M. Saheb S.M. Modelling evaporation using an artificial neural network algorithm. Hydrol. Processes 2002, 16(16):3189-3202. HYPRE3, 1099-1085, 10.1002/hyp.1096, and .
76. Supharatid S. Application of neural network model in establishing stage-discharge relationship for tidal river. Hydrol. Processes 2003, 17(15):3085-3099. HYPRE3, 1099-1085, 10.1002/hyp.1278.
77. Takagi T. Sugeno M. Fuzzy identification of systems and its application to modeling and control. IEEE Trans. Syst. Man Cybern. 1985, 15(1):116-132. ISYMAW, 0018-9472, and .
78. Tan S.B. K. Shuy E.B. Chua L.H. C. Modelling hourly and daily open-water evaporation rates in areas with an equatorial climate. Hydrol. Processes 2007, 21(4):486-499. HYPRE3, 1099-1085, 10.1002/hyp.6251, and .
79. Tayfur G. Artificial neural networks for sheet sediment transport. Hydrol. Sci. J. 2002, 47(6):879-892. HSJODN, 0262-6667, 10.1080/02626660209492997.
80. Terzi O. Keskin M.E. Evaporation estimation using gene expression programming. J. Appl. Sci. 2005, 5(3):508-512. JASODW, 1812-5654, 10.3923/jas.2005.508.512, and .
81. Trajkovic S. Temperature-based approaches for estimating reference evapotranspiration. J. Irrig. Drain Eng. 2005, 131(4):316-323. JIDEDH, 0733-9437, 10.1061/(ASCE)0733-9437(2005)131:4(316).