Predicting daily pan evaporation by soft computing models with limited climatic data;Prévoir l’évaporation journalière au bac par des modèles flous avec des données climatiques limitées

Hydrological Sciences Journal

Volumn 60, Issue 6, 2015, Pages 1120-1136

  Kim, Sungwon ^a   Shiri, Jalal ^b   Singh, Vijay P ^c   Kisi, Ozgur ^d   Landeras, Gorka ^e  

^a Dongyang University   (South Korea)

^b UNIVERSITY OF TABRIZ   (Iran)

^c Texas A M University   (United States)

^d CANIK BASARI UNIVERSITY   (Turkey)

^e NEIKER and a Vaccine producing Commercial Company CZVSL   (Spain)

Author keywords

limited climatic data; Mann Whitney U test; multiple linear regression; pan evaporation; soft computing models

Indexed keywords

CONFORMAL MAPPING; EVAPORATION; FORECASTING; GENE EXPRESSION; LINEAR REGRESSION; MULTILAYER NEURAL NETWORKS; NEURAL NETWORKS; POTABLE WATER; REGRESSION ANALYSIS; RESERVOIR MANAGEMENT; RESERVOIRS (WATER); SELF ORGANIZING MAPS; SOFT COMPUTING; STATISTICAL TESTS; THERMAL PROCESSING (FOODS); WATER MANAGEMENT; WATER RESOURCES; WATER SUPPLY; WATER SUPPLY SYSTEMS;

LIMITED CLIMATIC DATA; MANN-WHITNEY U TEST; MULTIPLE LINEAR REGRESSIONS; PAN EVAPORATION; SOFT COMPUTING MODELS;

CLIMATE MODELS;

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; CLIMATE EFFECT; EVAPORATION; HYPOTHESIS TESTING; MULTIPLE REGRESSION; PREDICTION; RESERVOIR; WATER MANAGEMENT; WATER SUPPLY;

IRAN;

EID: 84932605982     PISSN: 02626667     EISSN: 21503435     Source Type: Journal    
DOI: 10.1080/02626667.2014.945937     Document Type: Article

References (55)

1. R.G.Allen,, et al., 1998. Crop evapotranspiration guidelines for computing crop water requirements, FAO Irrigation and Drainage, Paper no. 56. Rome: Food and Agriculture Organization of the United Nations.
2. ASCE Task Committee on Definition of Criteria for Evaluation of Watershed Models, 1993. Criteria for evaluation of watershed models. Journal of Irrigation and Drainage Engineering, 119 (3), 429–442. doi:10.1061/(ASCE)0733-9437(1993)119:3(429).
3. B.M.Ayyub, and R.H.McCuen,, 2003. Probability, statistics, and reliability for engineers and scientists. 2nd ed. Boca Raton, FL: Taylor & Francis.
4. J.M.Bruton,, R.W.McClendon,, and G.Hoogenboom,, 2000. Estimating daily pan evaporation with artificial neural networks. Transactions of the ASAE, 43 (2), 491–496. doi:10.13031/2013.2730.
5. F.J.Chang,, et al., 2010. Assessing the effort of meteorological variables for evaporation estimation by self-organizing map neural network. Journal of Hydrology, 384 (1–2), 118–129. doi:10.1016/j.jhydrol.2010.01.016.
6. F.J.Chang,, L.C.Chang,, and Y.S.Wang,, 2007. Enforced self-organizing map neural networks for river flood forecasting. Hydrological Processes, 21 (6), 741–749. doi:10.1002/hyp.6262.
7. F.J.Chang,, W.Sun,, and C.H.Chung,, 2013. Dynamic factor analysis and artificial neural network for estimating pan evaporation at multiple stations in northern Taiwan. Hydrological Sciences Journal, 58 (4), 813–825. doi:10.1080/02626667.2013.775447.
8. B.J.Choudhury,, 1999. Evaluation of an empirical equation for annual evaporation using field observations and results from a biophysical model. Journal of Hydrology, 216 (1–2), 99–110. doi:10.1016/S0022-1694(98)00293-5.
9. V.T.Chow,, D.R.Maidment,, and L.W.Mays,, 1988. Applied hydrology. NewYork: McGraw-Hill.
10. C.W.Dawson, and R.L.Wilby,, 2001. Hydrological modelling using artificial neural networks. Progress in Physical Geography, 25 (1), 80–108. doi:10.1177/030913330102500104.
11. C.Ferreira,, 2001. Gene expression programming: a new adaptive algorithm for solving problems. Complex Systems, 13 (2), 87–129.
12. C.Ferreira,, 2006. Gene expression programming: mathematical modeling by an artificial intelligence. 2nd ed. Heidelberg: Springer.
13. J.W.Finch,, 2001. A comparison between measured and modelled open water evaporation from a reservoir in south-east England. Hydrological Processes, 15, 2771–2778. doi:10.1002/hyp.267.
14. H.V.Gupta,, et al., 2009. Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modeling. Journal of Hydrology, 377 (1–2), 80–91. doi:10.1016/j.jhydrol.2009.08.003.
15. A.Guven, and Ö.Kişi,, 2011. Daily pan evaporation modeling using linear genetic programming technique. Irrigation Science, 29 (2), 135–145. doi:10.1007/s00271-010-0225-5.
16. S.Haykin,, 2009. Neural networks and learning machines. 3rd ed. Upper Saddle River, NJ: Prentice Hall.
17. K.Hsu,, et al., 2002. Self-organizing linear output map (SOLO): an artificial neural network suitable for hydrologic modeling and analysis. Water Resources Research, 38 (12), 1302. doi:10.1029/2001WR000795.
18. S.K.Jain,, P.C.Nayak,, and K.P.Sudheer,, 2008. Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation. Hydrological Processes, 22 (13), 2225–2234. doi:10.1002/hyp.6819.
19. A.L.Kay, and H.N.Davies,, 2008. Calculating potential evaporation from climate model data: a source of uncertainty for hydrological climate change impacts. Journal of Hydrology, 358 (3–4), 221–239. doi:10.1016/j.jhydrol.2008.06.005.
20. M.E.Keskin, and Ö.Terzi,, 2006. Artificial neural network models of daily pan evaporation. Journal of Hydrologic Engineering, 11 (1), 65–70. doi:10.1061/(ASCE)1084-0699(2006)11:1(65).
21. S.Kim, and H.S.Kim,, 2008. Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling. Journal of Hydrology, 351 (3–4), 299–317. doi:10.1016/j.jhydrol.2007.12.014.
22. S.Kim,, et al., 2013. Estimating daily pan evaporation using different data-driven methods and lag-time patterns. Water Resources Management, 27 (7), 2267–2286. doi:10.1007/s11269-013-0287-2.
23. S.Kim,, J.H.Kim,, and K.B.Park,, 2009. Statistical learning theory for the disaggregation of the climatic data. In: Proceedings of 33rd IAHR Congress 2009, 9–14 August. Vancouver: IAHR, 1154–1162.
24. S.Kim,, J.Shiri,, and O.Kisi,, 2012. Pan evaporation modeling using neural computing approach for different climatic zones. Water Resources Management, 26 (11), 3231–3249. doi:10.1007/s11269-012-0069-2.
25. Ö.Kişi,, 2006. Daily pan evaporation modelling using a neuro-fuzzy computing technique. Journal of Hydrology, 329 (3–4), 636–646. doi:10.1016/j.jhydrol.2006.03.015.
26. O.Kişi,, 2007. Evapotranspiration modelling from climatic data using a neural computing technique. Hydrological Processes, 21, 1925–1934. doi:10.1002/hyp.6403.
27. Ö.Kişi,, 2009. Modeling monthly evaporation using two different neural computing techniques. Irrigation Science, 27 (5), 417–430. doi:10.1007/s00271-009-0158-z.
28. O.Kişi,, A.Pour-Ali Baba,, and J.Shiri,, 2012. Generalized neurofuzzy models for estimating daily pan evaporation values from weather data. Journal of Irrigation and Drainage Engineering, 138 (4), 349–362. doi:10.1061/(ASCE)IR.1943-4774.0000403.
29. T.Kohonen,, 1990. The self-organizing map. Proceedings of the IEEE, 78 (9), 1464–1480. doi:10.1109/5.58325.
30. T.Kohonen,, 2001. Self-organizing maps. NewYork: Springer-Verlag.
31. N.T.Kottegoda, and R.Rosso,, 1997. Statistics, probability, and reliability for civil and environmental engineers. Singapore: McGraw-Hill.
32. M.Kumar,, et al., 2002. Estimating evapotranspiration using artificial neural networks. Journal of Irrigation and Drainage Engineering, 128 (4), 224–233. doi:10.1061/(ASCE)0733-9437(2002)128:4(224).
33. D.R.Legates, and G.J.McCabe,, 1999. Evaluating the use of goodness-of-fit measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35 (1), 233–241. doi:10.1029/1998WR900018.
34. G.F.Lin, and L.H.Chen,, 2005. Time series forecasting by combining the radial basis function network and the self-organizing map. Hydrological Processes, 19 (10), 1925–1937. doi:10.1002/hyp.5637.
35. G.F.Lin, and L.H.Chen,, 2006. Identification of homogeneous regions for regional frequency analysis using the self-organizing map. Journal of Hydrology, 324 (1–4), 1–9. doi:10.1016/j.jhydrol.2005.09.009.
36. G.F.Lin, and M.C.Wu,, 2007. A SOM-based approach to estimating design hyetographs of ungauged sites. Journal of Hydrology, 339 (3–4), 216–226. doi:10.1016/j.jhydrol.2007.03.016.
37. G.F.Lin, and M.C.Wu,, 2009. A hybrid neural network model for typhoon-rainfall forecasting. Journal of Hydrology, 375 (3–4), 450–458. doi:10.1016/j.jhydrol.2009.06.047.
38. R.H.McCuen,, 1993. Microcomputer applications in statistical hydrology. Englewood Cliffs, NJ: Prentice Hall.
39. J.E.Nash, and J.V.Sutcliffe,, 1970. River flow forecasting through conceptual models part I — a discussion of principles. Journal of Hydrology, 10 (3), 282–290. doi:10.1016/0022-1694(70)90255-6.
40. NeuroDimension Inc, 2005. Developers of neurosolutions V5.01: neural network simulator. Gainesville, FL: NeuroDimension Incorporated.
41. H.L.Penman,, 1948. Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 193, 120–145. doi:10.1098/rspa.1948.0037.
42. J.C.Principe,, N.R.Euliano,, and W.C.Lefebvre,, 2000. Neural and adaptive systems: fundamentals through simulation. NewYork: Wiley, John & Sons, Inc.
43. D.O.Rosenberry,, et al., 2007. Comparison of 15 evaporation methods applied to a small mountain lake in the northeastern USA. Journal of Hydrology, 340 (3–4), 149–166. doi:10.1016/j.jhydrol.2007.03.018.
44. J.Shiri, and Ö.Kişi,, 2011. Application of artificial intelligence to estimate daily pan evaporation using available and estimated climatic data in the Khozestan Province (Southwestern Iran). Journal of Irrigation and Drainage Engineering, 137 (7), 412–425. doi:10.1061/(ASCE)IR.1943-4774.0000315.
45. J.Shiri,, et al., 2011. Estimating daily pan evaporation from climatic data of the state of Illinois, USA using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Hydrology Research, 42 (6), 491–502. doi:10.2166/nh.2011.020.
46. J.Shiri,, P.Marti,, and V.P.Singh,, 2014. Evaluation of gene expression programming approaches for estimating daily pan evaporation through spatial and temporal data scanning. Hydrological Processes, 28 (3), 1215–1225. doi:10.1002/hyp.9669.
47. V.P.Singh,, S.K.Jain,, and A.Tyagi,, 2007. Risk and reliability analysis: a handbook for civil and environmental engineers. Reston, VA: ASCE Press.
48. B.Sivakumar,, A.W.Jayawardena,, and T.M.K.G.Fernando,, 2002. River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches. Journal of Hydrology, 265 (1–4), 225–245. doi:10.1016/S0022-1694(02)00112-9.
49. K.P.Sudheer,, et al., 2002. Modelling evaporation using an artificial neural network algorithm. Hydrological Processes, 16 (16), 3189–3202. doi:10.1002/hyp.1096.
50. K.P.Sudheer,, A.K.Gosain,, and K.S.Ramasastri,, 2003. Estimating actual evapotranspiration from limited climatic data using neural computing technique. Journal of Irrigation and Drainage Engineering, 129 (3), 214–218. doi:10.1061/(ASCE)0733-9437(2003)129:3(214).
51. H.Tabari,, S.Marofi,, and A.A.Sabziparvar,, 2010. Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression. Irrigation Science, 28 (5), 399–406. doi:10.1007/s00271-009-0201-0.
52. S.B.K.Tan,, E.B.Shuy,, and L.H.C.Chua,, 2007. Modelling hourly and daily open-water evaporation rates in areas with an equatorial climate. Hydrological Processes, 21 (4), 486–499. doi:10.1002/hyp.6251.
53. Ö.Terzi, and M.Erol Keskin,, 2005. Evaporation estimation using gene expression programming. Journal of Applied Sciences, 5 (3), 508–512. doi:10.3923/jas.2005.508.512.
54. A.S.Tokar, and P.A.Johnson,, 1999. Rainfall-runoff modeling using artificial neural networks. Journal of Hydrologic Engineering, 4 (3), 232–239. doi:10.1061/(ASCE)1084-0699(1999)4:3(232).
55. C.Vallet-Coulomb,, et al., 2001. Lake evaporation estimates in tropical Africa (Lake Ziway, Ethiopia). Journal of Hydrology, 245 (1–4), 1–18. doi:10.1016/S0022-1694(01)00341-9.