Empirical Penman-Monteith equation and artificial intelligence techniques in predicting reference evapotranspiration: A review

International Journal of Water

Volumn 10, Issue 1, 2016, Pages 55-66

  Abdullah, Shafika Sultan ^a,b   Malek, Marlinda Abdul ^a  

^a Universiti Tenaga Nasional   (Malaysia)

^b Dohuk Polytechnic University   (Iraq)

Author keywords

ANN; Artificial neural network; ELM; Evapotranspiration; Extreme learning machine; FAO Penman Monteith equation

Indexed keywords

AGRICULTURAL ROBOTS; EVAPOTRANSPIRATION; NEURAL NETWORKS; WATER SUPPLY; WEATHER FORECASTING;

ARTIFICIAL INTELLIGENCE TECHNIQUES; EXTREME LEARNING MACHINE; FAO PENMAN MONTEITHS; FOOD AND AGRICULTURE ORGANIZATIONS; IRRIGATION PROJECTS; PENMAN-MONTEITH EQUATIONS; REFERENCE EVAPOTRANSPIRATION; WEATHER PARAMETERS;

LEARNING SYSTEMS;

ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; CLIMATE CONDITIONS; EMPIRICAL ANALYSIS; EVAPOTRANSPIRATION; FOOD AND AGRICULTURAL ORGANIZATION; MACHINE LEARNING; PENMAN-MONTEITH EQUATION; PREDICTION; UNITED NATIONS;

EID: 84953339870     PISSN: 14656620     EISSN: 17415322     Source Type: Journal    
DOI: 10.1504/IJW.2016.073741     Document Type: Review

References (63)

1. Allen, R. G. and Pruitt, W. O. (1986) 'Rational use of the FAO Blaney-Criddle formula', Journal of Irrigation and Drainage Engineering, ASCE, Vol. 112(IR2), pp. 139-155.
2. Allen, R. G., Pereira, L. S., Raes, D. and Smith, M. (1998) 'Crop evapotranspiration-guidelines for computing crop water requirements', Irrigation and Drainage, Paper No. 56, Food and Agriculture Organization of the United Nations, Rome, Vol. 300, p. 6541.
3. Basheer, I. A. and Hajmeer, M. (2000) 'Artificial neural networks: fundamentals, computing, design, and application', Journal of Microbiological Methods, Vol. 43, No. 1, pp. 3-31.
4. Bishop, C. M. (1994) 'Neural networks and their applications', Review of Scientific Instruments, Vol. 65, No. 6, pp. 1803-1832.
5. Blaney, H. F. and Criddle, W. D. (1950) Determining Water Requirements in Irrigated Areas from Climatological and Irrigation Data, US Soil Conservation Service Technical Paper 96, 48 pp.
6. Burba, G. (2013) Eddy Covariance Method for Scientific, Industrial, Agricultural, and Regulatory Applications, 1st ed., 331pp, LI-COR Biosciences, Lincoln, Nebraska, USA.
7. Cancelliere, A., Giusiano, G., Ancarani, A. and Rossi, G. (2002) 'A neural networks for deriving irrigation reservoir operating rules', Water Resources Management, Vol. 16, No. 1, pp. 71-88.
8. Cigizoglu, H. K. (2003) 'Estimation, forecasting and extrapolation of river flows by artificial neural networks', Hydrological Sciences Journal, Vol. 48, No. 3, pp. 349-361.
9. Citakoglu, H., Cobaner, M., Haktanir, T. and Kisi, O. (2014) 'Estimation of monthly mean reference evapotranspiration in Turkey', Water Resources Management, Vol. 28, No. 1, pp. 99-113.
10. De Bruin, H. A. R., Kohsiek, W. and Van Den Hurk, B. J. J. M. (1993) 'A verification of some methods to determine the fluxes of momentum, sensible heat, and water vapour using standard deviation and structure parameter of scalar meteorological quantities', 1st ed., Boundary-Layer Meteorology, Vol. 63, No. 3, pp. 231-257.
11. Doorenbos, J. and Pruitt, W. O. (1977) 'Guidelines for predicting crop water requirements', Food and Agricultural Organization, Irrigation and Drainage, Paper No. 24, 144 pp.
12. El-Baroudy, A. Elshorbagy, Carey, S. K., Giustolisi, O. and Savic, D. (2010) 'Comparison of three data-driven techniques in modelling the evapotranspiration process', Journal of Hydro Informatics, Vol. 12, No. 4, pp. 365-379.
13. Eslamian, S. S., Gohari, S. A., Zariean, M. J. and Firoozfar, A. (2012) 'Estimating Penman-Monteith reference evapotranspiration using artificial neural networks and genetic algorithm: a case study', Arabian Journal for Science and Engineering, Vol. 37, No. 4, pp. 935-944.
14. Foken, T. (2008) Micrometeorology, 308 pp., Springer-Verlag, Berlin, Germany.
15. Garrat, J. R. and Hicks, B. B. (1973) 'Momentum, heat and water vapor transfer to and from natural and artificial surfaces', Quarterly Journal of the Royal Meteorological Society, Vol. 99, No. 422, pp. 68-687.
16. Ga.water.usgs.gov. (2013) Evaporation, The Water Cycle, from USGS Water-Science School [online] http://ga.water.usgs.gov/edu/watercycleevaporation. html (accessed 24 June 2013).
17. Hargreaves, G. H. and Samani, Z. A. (1985) 'Reference crop evapotranspiration from temperature', American Society of Agricultural Engineers (Microfiche Collection) (USA), No. Fiche No. 85-2517.
18. Huang, G-B., Zhu, Q-Y. and Siew, C-K. (2006) 'Extreme learning machine: theory and applications', Neurocomputing, Vol. 70, No. 1, pp. 489-501.
19. Jain, S. K., Singh, V. P. and van Genuchten, M. Th. (2004) 'Analysis of soil water retention data using artificial neural networks', Journal of Hydrologic Engineering, Vol. 9, No. 5, pp. 415-420.
20. Jensen, M. E. and Haise, H. R. (1963) 'Estimating evapotranspiration from solar radiation', Journal of Irrigation and Drainage Engineering, ASCE, Vol. 89, No. 4, pp. 15-41.
21. Jensen, M. E., Burman, R. D. and Allen, R. G. (1990) Evapotranspiration and Irrigation Water Requirements, ASCE, New York, Manual and Reports on Engineering Practice No. 70, 332 p.
22. Khoshhal, J. and Mokarram, M. (2012) 'Model for prediction of evapotranspiration using MLP neural network', International Journal of Environmental Sciences, Vol. 3, No. 3, pp. 1000-1009.
23. Kim, S. and Kim, H. S. (2008) 'Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modelling', Journal of Hydrology, Vol. 351, Nos. 3-4, pp. 299-317.
24. Kiranyaz, S., Ince, T., Yildirim, A. and Gabbouj, M. (2009) 'Evolutionary artificial neural networks by multi-dimensional particle swarm optimization', Neural Networks, Vol. 22, No. 10, pp. 1448-1462.
25. Kisi, O. (2004a) 'River flow modeling using artificial neural networks', Journal of Hydrologic Engineering, ASCE, Vol. 9, No. 1, pp. 60-63.
26. Kisi, O. (2004b) Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation', Hydrological Science Journal, Vol. 49, No. 6, pp. 1025-1040.
27. Kisi, O. (2005) 'Suspended sediment estimation using neuro-fuzzy and neural network approaches', Hydrological Sciences Journal, Vol. 50, No. 4, pp. 683-696.
28. Kisi, O. (2006) 'Generalized regression neural networks for evapotranspiration modelling', Journal of Hydrological Sciences, 51, No. 6, pp. 1092-1105.
29. Kisi, O. (2008) 'The potential of different ANN techniques in evapotranspiration modelling', Hydrology Process, Vol. 22, No. 14, pp. 2449-2460.
30. Kisi, O. (2009) 'Neural networks and wavelet conjunction model for intermittent stream flow forecasting', Journal of Hydrologic Engineering, Vol. 14, No. 8, pp. 773-782.
31. Kisi, O. (2010) 'Fuzzy genetic approach for modeling reference evapotranspiration', Journal of Irrigation and Drainage Engineering, ASCE, Vol. 136, No. 3, pp. 175-183.
32. Kisi, O. and Oztürk, O. (2007) 'Adaptive neurofuzzy computing technique for evapotranspiration estimation', Journal of Irrigation and Drainage Engineering, Vol. 133, No. 4, pp. 368-379.
33. Kumar, M., Raghuwanshi, N., Singh, R., Wallender, W. and Pruitt, W. (2002) 'Estimating evapotranspiration using artificial neural network', Journal of Irrigation and Drainage Engineering, ASCE, Vol. 128, No. 4, pp. 224-233.
34. Landeras, G., Ortiz-Barredo, A. and Lopez, J. J. (2008) 'Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain) ', Journal of Agricultural Water Management, Vol. 95, No. 5, pp. 553-565.
35. Maier, H. R. and Dandy, G. C. (2000) 'Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications', Environmental Modeling Software, Vol. 15, No. 1, pp. 101-124.
36. Monteith, J. and Unsworth, M. (2013) Principles of Environmental Physics, 4th ed., 401 pp., Elsevier Science, Burlington.
37. Monteith, J. L. (1985) 'Evaporation from land surfaces: progress in analysis and prediction since 1948', in Advances in Evapotranspiration, Proc. National Conference on Advances in Evapotranspiration, December 16-17, Chicago, IL, ASAE, St. Joseph, MI, pp. 4-12.
38. Munro, D. S. (1987) 'Surface conductance to evaporation from a wooded swamp', Agriculture and Forest Meteorology, Vol. 41, Nos. 3-4, pp. 249-25.
39. Penman, H. L. (1948) 'Natural evaporation from open water, bare soil and grass', Proc. Royal Soc. London, A: Math. Phys. Sci., Vol. 193, No. 1032, pp. 120-145.
40. Penman, H. L. (1963) Vegetation and Hydrology, Tech. Comm. No. 53, Commonwealth Bureau of Soils, Harpenden, England, 125 pp.
41. Pereira, L. S., Perrier, A., Allen, R. G. and Alves, I. (1999) 'Evapotranspiration: review of concepts and future trends', Journal of Irrigation and Drainage Engineering, ASCE, Vol. 125, No. 2, pp. 45-51.
42. Rana, G. and Katerji, N. (2000) 'Measurement and estimation of actual evapotranspiration in the field under Mediterranean climate: a review', European Journal of Agronomy, Vol. 13, pp. 125-153.
43. Rumelhart, D. E. and McClelland, J. L. (1986) Parallel Distributed Processing-exploration in the Microstructures of Cognition, Vol. II, MIT Press, Cambridge, MA.
44. Shahrokhnia, M. H. and Sepaskhaha, A. R. (2012) 'Evaluation of wheat and maize evapotranspiration determination by direct use of the Penman-Monteith equation in a semi-arid region', Archives of Agronomy and Soil Science, Vol. 58, No. 1, pp. 1283-1302.
45. Shiri, J., Kim, S. and Kisi, O. (2013) Estimation of Daily Dew Point Temperature using Genetic Programming and Neural Networks Approaches, Hydrology Research, IWA Publishing.
46. Shiri, J., Kisi, O., Landeras, G., López, J. J., Nazemi, A. H. and Stuyt, L. C. P. M. (2012) 'Daily reference evapotranspiration modeling by using genetic programming approach in the Basque Country (Northern Spain) ', Journal of Hydrology, Vols. 414-415, pp. 302-316, DOI: 10.1016/j.jhydrol.2011.11.004.
47. Siah Yap, K. and Jen Yap, H. (2012) 'Daily maximum load forecasting of consecutive national holidays using OSELM-based multi-agents system with weighted average strategy', Neurocomputing, Vol. 81, pp. 108-112, DOI: 10.1016/j.neucom.2011.12.002.
48. Smith, M., Allen, R. G., Monteith, J. L., Perrier, A., Pereira, L. and Segeren, A. (1992) Report of the Expert Consultation on Procedures for Revision of FAO Guidelines for Prediction of Crop Water Requirements, Food and Agriculture Organization of the United Nations, Rome, 54 pp.
49. Subedi, A., Chávez, J. L. and Andales, A. A. (2013) 'Preliminary performance evaluation of the Penman-Monteith evapotranspiration equation in southeastern Colorado', Hydrology Days, pp. 84-90.
50. Sudheer, K. P., Nayak, P. C. and Ramasastri, K. S. (2003b) 'Improving peak flow estimates in artificial neural network', Hydrological Processes, Vol. 17, No. 3, pp. 677-686.
51. Suliman, A., Nazir, N., Othman, M., Malek, M. A. and Ku-Mahmud, K. R. (2013) 'Artificial neural network and support vector machine in flood forecasting: a review', Proc. 4th International Conference on Computing and Informatics (ICOCI), Sarawak, Malaysia, Universiti Utara Malaysia, Paper No. 30, pp. 327-332.
52. Supharatid, S. (2003) 'Application of a neural network model in establishing a stage-discharge relationship for a tidal river', Hydrological Processes, Vol. 17, No. 15, pp. 3085-3099.
53. Tabari, H. and Talaee, P. (2013) 'Multilayer perceptron for reference evapotranspiration estimation in a semiarid region', Neural Computing and Applications, Vol. 23, No. 2, pp. 341-348.
54. Tayfur, G. (2002) 'Artificial neural networks for sheet sediment transport', Hydrological Sciences Journal, 47, No. 6, pp. 879-892.
55. Thornthwaite, C. W. (1948) 'An approach toward a rational classification of climate', Geographical Review, Vol. 38, No. 1, pp. 55-94.
56. Thornthwaite, C. W. and Holtman, B. (1942) 'Measurement of evaporation from the land and water surface', USDA, Bull., p. 817.
57. Trajkovic, S. (2005) 'Temperature-Based approaches for estimating reference evapotranspiration', Journal of Irrigation and Drainage Engineering, ASCE, Vol. 131, No. 4, pp. 316-323.
58. Traore, S., Wang, Y. and Kerh, T. (2010) 'Artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian Zone', Journal of Agricultural Water Management, Vol. 97, No. 5, pp. 707-714.
59. Walter, I. A., Allen, R. G., Elliott, R., Jensen, M. E., Itenfisu, D., Mecham, B., Howell, T. A., Snyder, R., Brown, P., Echings, S., Spofford, T., Hattendorf, M., Cuenca, R. H., Wright, J. and Martin, D. (2000) 'ASCE's standardized reference evapotranspiration equation', Watershed Management and Operations Management, pp. 1-11, in ASCE Conference Proceedings, 105 pp., DOI 10.1061/40499, No. 2000 126.
60. Warner, B. and Misra, M. (1996) 'Understanding neural networks as statistical tools', The American Statistician, Vol. 50, No. 4, pp. 284-293.
61. Wright, J. L. (1988) 'Daily and seasonal evapotranspiration and yield of irrigated alfalfa in Southern Idaho', Agronomy Journal, Vol. 80, No. 4, pp. 662-669.
62. Yoder, R. E., Odhiambo, L. O. and Wright, W. C. (2005) 'Evaluation of methods for estimating daily reference crop evapotranspiration at a site in the Humid Southeast United States', Soil and Water Division, ASAE, Vol. 21, No. 2, pp. 197-220.
63. Zurada, J. M. (1992) Introduction to Artificial Neural Networks, 1st ed., West Publishing, St. Paul.