Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques

Advances in Water Resources

Volumn 32, Issue 1, 2009, Pages 88-97

  Moghaddamnia, A ^a   Ghafari Gousheh, M ^a   Piri, J ^b   Amin, S ^c   Han, D ^d  

^a UNIVERSITY OF ZABOL   (Iran)

^b Management of Agriculture   (Iran)

^c SHIRAZ UNIVERSITY   (Iran)

^d UNIVERSITY OF BRISTOL   (United Kingdom)

Author keywords

Adaptive neuro fuzzy inference system; Artificial neural networks; Evaporation; Gamma test; Input data selection

Indexed keywords

BACKPROPAGATION; DATA REDUCTION; EVAPORATION; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; MOISTURE; NETWORK MANAGEMENT; PUBLIC KEY CRYPTOGRAPHY; STATISTICAL TESTS; VAPORS; WATER RESOURCES;

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS; ANN MODELS; ARTIFICIAL NEURAL NETWORKS; CLIMATIC REGIONS; DATA AVAILABILITIES; DATA INPUTS; DATA POINTS; DATA SELECTIONS; EMPIRICAL FORMULAS; EVAPORATION ESTIMATIONS; EVAPORATION PROCESSES; GAMMA TEST; GAMMA TESTS; HYDROLOGIC CYCLES; INPUT DATA SELECTION; INPUT DATUMS; MODEL CALIBRATIONS; SEMI-ARID; TEST DATUMS; VALIDATION DATUMS; WATER RESOURCES DEVELOPMENTS;

NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; ESTIMATION METHOD; EVAPORATION; FUZZY MATHEMATICS; MODEL VALIDATION; NUMERICAL MODEL; PERFORMANCE ASSESSMENT;

EID: 57549095413     PISSN: 03091708     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.advwatres.2008.10.005     Document Type: Article

References (75)

1. McCuen R.H. Hydrologic analysis and design (1998), Prentice Hall, Englewood Cliffs, New Jersey
2. Kisi O. The potential of different ANN techniques in evapotranspiration modelling. Hydrol Process 22 (2008) 2449-2460 10.1002/hyp.6837
3. Thornthwaite C.W. An approach toward a rational classification of climate. Geograph Rev 38 (1948) 55-94
4. Turc L. Estimation of irrigation water requirements, potential evapotranspiration: a simple climatic formula evolved up to date. Ann Agron 12 (1961) 13-49
5. Romanenko VA. Computation of the autumn soil moisture using a universal relationship for a large area. In: Proceedings of the Ukrainian hydrometeorological research institute, Kiev, Ukraine; 1961. p. 3.
6. Penman H.L. Natural evaporation from open water, bare soil, and grass. Proc Roy Soc London 193 (1948) 120-145
7. McKenzie R.S., and Craig A.R. Evaluation of river losses from the Orange River using hydraulic modelling. J Hydrol 241 1-2 (2001) 62-69
8. Abtew W. Evaporation estimation for Lake Okeechobee in south Florida. J Irrig Drain Eng 127 3 (2001) 140-147
9. Hanson C.L. Prediction of Class A pan evaporation in southwest southwest Idaho. J Irrig Drain Eng 115 2 (1989) 166-171
10. Knapp H.V., Yu Y.-.S., and Pogge E.C. Monthly evaporation for Milford Lake in Kansas. J Irrig Drain Eng 110 2 (1984) 138-148
11. Warnaka K., and Pochop L. Analyses of equations for free water evaporation estimates. Water Resour Res 24 7 (1988) 979-984
12. Warnaka K., and Pochop L. Neural networks for runoff prediction. J Hydrol Eng 5 4 (1988) 424-427
13. Choudhury B.J. Evaluation of empirical equation for annual evaporation using field observations and results from a biophysica model. J Hydrol 216 1-2 (1999) 99-110
14. Jang J.-.S.R. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23 3 (1993) 665-685
15. Cybenko G. Approximations by superpositions of a sigmoidal function. Math Control Signals Syst 2 (1989) 303-314
16. Hornik K., Stinnchcombe M., and White H. Multi-layer feed forward networks are universal approximators. Neural Networks 2 (1989) 359-366
17. Barron AR. Universal approximation bonds for superpositions of a sigmoidal function. Technical report No. 58, Department of Statistics, University of Illinois, Urbana Champaign; 1991.
18. Mosner MS, Aulenbach BT. Comparison of methods used to estimate lake evaporation for a water budget of lake Seminole, southwestern Georgia and northwestern Florida. In: Proceedings of the 2003 Georgia water resources conference, April 23-24, 2003, University of Georgia; 2003.
19. Yu Y.S., and Knapp H.V. Weekly, monthly, and annual evaporations for Elk City Lake. J Hydrol 80 (1985) 93-110
20. Ikebuchi S., Seki M., and Ohtoh A. Evaporation from lake Biwa. J Hydrol 102 (1988) 427-444
21. Laird N.F., and Kristovich D.A.R. Variations of sensible and latent heat fluxes from a great lakes buoy and associated synoptic weather patterns. J Hydrometeorol 3 1 (2002) 3-12
22. Laird N.F., and Kristovich D.A.R. Variations of sensible and latent heat fluxes from a great lakes buoy and associated synoptic weather patterns. J Hydrometeorol 3 1 (2002) 3-12
23. Hostetler S.W., and Bartlein P.J. Simulation of lake evaporation with application to modelling lake level variations of Harney-Malheur Lake, Oregon. Water Resour Res 26 10 (1990) 2603-2612
24. Blodgett T.A., Lenters J.D., and Isacks B.L. Constraints on the origin of paleo lake expansions in the Central Andes. Earth Interact 1 (1997)
25. Winter T.C. Uncertainties in estimating the water balance of lakes. Water Resour Bull 17 1 (1981) 82-115
26. Blanken P.D., Rouse W.R., Culf A.D., Spence C., Boudreau L.D., Jasper J.N., et al. Eddy covariance measurements of evaporation from Great Slave Lake, Northwest Territories, Canada. Water Resour Res 36 4 (2000) 1069-1077
27. Han H., and Felker P. Estimation of daily soil water evaporation using an artificial neural network. J Arid Environ 37 (1997) 251-260
28. Kumar M., Raghuwanshi N.S., Singh R., Wallender W.W., and Pruitt W.O. Estimating evapotranspiration using artificial neural network. J Irrig Drain Eng 128 4 (2002) 224-233
29. Sudheer K.P., Gosain A.K., Rangan D.M., and Saheb S.M. Modelling evaporation using an artificial neural network algorithm. Hydrol Process 16 (2002) 3189-3202
30. Terzi O., and Keskin M.E. Modelling of daily pan evaporation. J Appl Sci 5 2 (2005) 368-372
31. Kisi O. Daily pan evaporation modelling using a neuro-fuzzy computing technique. J Hydrol 329 636-646 (2006) 10.1016/j.jhydrol.2006.03.015
32. Keskin M.E., and Terzi O. Artificial neural network models of daily pan evaporation. J Hydrol Eng ASCE 1 65 (2006) 65-70 10.1061/(ASCE)1084-0699(2006)11 1(65)
33. Terzi O.M., Erol Keskin M., and Dilek Taylan E. Estimation evaporation using Anfis. J Irrig Drain Eng 132 5 (2006) 503-507
34. Tan S.B.K., Shuy E.B., and Chua L.H.C. Modelling hourly and daily open-water evaporation rates in areas with an equatorial climate. Hydrol Process 21 486-499 (2007) 10.1002/hyp.6251
35. Shu Chang, Taha BMJ, Ouarda. Estimation régionale des débits de crues par la méthode Anfis. Rapport de recherche N R- 930. 41p.
36. Hsu K., Gupta H.V., and Sorooshian S. Artificial neural network modelling of the rainfall-runoff process. Water Resour Res 31 10 (1995) 159-169
37. Fernando D.A., and Jayawardena A.W. Runoff forecasting using RBF network with OLS algorithm. J Hydrol Eng 3 3 (1988) 203-209
38. Tokar A.S., and Johnson P.A. Rainfall-runoff modelling using artificial neural networks. J Hydrol Eng 4 3 (1999) 232-239
39. Clair T.A., and Ehrman J.M. Using neural networks to assess the influence of changing seasonal climates in modifying discharge, dissolved organic carbon, and nitrogen export in eastern Canadian rivers. Water Resour Res 34 3 (1998) 447-455
40. Imrie C.E., Durucan S., and Korre A. River flow prediction using artificial neural networks generalisation beyond the calibration range. J Hydrol 233 (2000) 138-153
41. Han D., Kwong T., and Li S. Uncertainties in real-time flood forecasting with neural networks. Hydrol Process 21 (2007) 223-228 10.1002/hyp.6184
42. Farnsworth RK, Thompson ES. Mean monthly, seasonal, and annual pan evaporation for the United States. NOAA Technical Report NWS 34, Washington, DC; 1982. 82p.
43. Rosenberg N.J., Blad B.L., and Verma S.B. Microclimate: the biological environment. 2nd ed. (1983), John Wiley & Sons, New York 495p.
44. McCulloch W.S., and Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5 (1943) 115-133
45. Anderson Dave, McNeill George. Artificial neural networks technology; 1992.
46. In: Judith E.D. (Ed). Neural network architectures (1990), Van Nostard Reinhold, New York
47. Rumelhart D.E., Hinton G.E., and Williams R.J. Learning internal representations by back propagation. In: Rumelhart D.E., McClelland J.L., and PDP Research Group (Eds). Parallel distributed processing vol. 1 (1986), MIT Press, Cambridge (MA) 318-362
48. Chang L.-C., and Chang F.-J. Intelligent control for modelling of real-time reservoir operation. Hydrol Process 15 9 (2001) 1621-1634
49. Tsoukalas L.H., and Uhrig R.E. Fuzzy and neural approaches in engineering (1997), Wiley-Interscience, New York p. 587
50. Lin C.T., and Lee C.S.G. Neural fuzzy systems (1995), Prentice Hall PTR, Englewood Cliffs (NJ)
51. Khu S.T., and Chan W.T. Derivation of pareto front with genetic algorithm and neural network. J Hydrol Eng 6 1 (2001) 52-61
52. Končar N. Optimisation methodologies for direct inverse neurocontrol. PhD thesis, Department of Computing, Imperial College of Science, Technology and Medicine, University of London; 1997.
53. Agalbjörn S., Končar N., and Jones A.J. A note on the gamma test. Neural Comput Appl 5 3 (1997) 131-133 ISSN 0-941-0643
54. Chuzhanova N.A., Jones A.J., and Margetts S. Feature selection for genetic sequence classification. Bioinformatics 14 2 (1998) 139-143
55. Oliveira AG. Synchronisation of chaos and applications to secure communications. PhD thesis, Department of Computing, Imperial College, University of London, UK; 1999.
56. Tsui APM. Smooth data modelling and stimulus-response via stabilisation of neural chaos. PhD thesis, Department of Computing, Imperial College of Science, Technology and Medicine, University of London; 1999.
57. Tsui A.P.M., Jones A.J., and de Oliveira A.G. The construction of smooth models using irregular embeddings determined by a gamma test analysis. Neural Comput Appl 10 4 (2002) 318-329 10.1007/s005210200004
58. Durrant PJ. winGamma: a non-linear data analysis and modelling tool with applications to flood prediction. PhD thesis, Department of Computer Science, Cardiff University, Wales, UK; 2001.
59. Jones A.J., Tsui A., and de Oliveira A.G. Neural models of arbitrary chaotic systems: construction and the role of time delayed feedback in control and synchronization. Complexity Int 09 (2002)
60. Evans D., and Jones A.J. A proof of the gamma test. Proc Roy Soc Ser A 458 2027 (2002) 2759-2799
61. Corcoran J., Wilson I., and Ware J. Predicting the geo-temporal variation of crime and disorder. Int J Forecast 19 (2003) 623-634 10.1016/S01692070(03)00095-5
62. Karunanithi N., Grenney W.J., Whitley D., and Bovee K. Neural networks for river flow prediction. J Comput Civil Eng 8 2 (1994) 201-220
63. Anderson D.B. Relative humidity or vapor pressure deficit. Ecology 17 2 (1936)
64. Bray M., and Han D. Identification of support vector machines for runoff modelling. J Hydroinform 6 4 (2004) 265-280
65. Han D., Chan L., and Zhu N. Flood forecasting using support vector machines. J Hydroinform 9 4 (2007) 267-276 10.2166/hydro.2007.027
66. Alizadeh A. Principles of applied hydrology, Imam Reza University publication; 2004 [in Persian].
67. Haykin S. Neural networks - a comprehensive foundation. 2nd ed. (1999), Prentice Hall
68. HYREX, Hydrological radar experiment; 2008. .
69. Hammond M., and Han D. Quantization analysis of weather radar data with synthetic rainfall. Stochast Environ Res Risk Assess 22 3 (2008) 1436-3259 10.1007/s00477-007-0124-3
70. Wang Y.C., Han D., Yu P.S., and Cluckie I.D. Comparative modeling of two catchments in Taiwan and England. Hydrol Process 6512 20 (2006) 4335-4349 10.1002/hyp
71. Hammond M., and Han D. Recession curve estimation for storm event separation. J Hydrol 330 (2006) 573-585 10.1016/j.jhydrol.2006.04.027
72. Hammond M., and Han D. Issues of using digital maps for catchment delineation. Proc ICE Water Manage 159 1 (2006) 45-51 10.1680/wama.2006.159.1.45
73. Yang Z., and Han D. Derivation of unit hydrograph using a transfer function approach. Water Resour Res 42 (2006) W01501 10.1029/2005WR004227
74. Han D., Cluckie I.D., Griffith R.J., and Austin G. Rainfall measurements for urban catchments using weather radars. J Urban Technol 7 1 (2000) 85-102 10.1080/713684102
75. Han D., Cluckie I.D., Karbassioun D., Lawry J., and Krauskopf B. River flow modelling using fuzzy decision trees. Water Resour Manage 16 6 (2002) 10.1023/A:1022251422280