Multi-lead ahead prediction model of reference evapotranspiration utilizing ANN with ensemble procedure

Stochastic Environmental Research and Risk Assessment

Volumn 27, Issue 6, 2013, Pages 1423-1440

  El Shafie, Ahmed ^a   Alsulami, Humod Mosad ^a,c   Jahanbani, Heerbod ^b   Najah, Ali ^a  

^a UNIVERSITI KEBANGSAAN MALAYSIA   (Malaysia)

^b UNIVERSITY OF MELBOURNE   (Australia)

^c Ministry of Education Saudi Arabia   (Saudi Arabia)

Author keywords

Ensemble neural network; Evapotranspiration; Neural network; Over fitting; Rasht City (Iran)

Indexed keywords

ACCURATE PREDICTION; ENSEMBLE NEURAL NETWORK; MAXIMUM AND MINIMUM TEMPERATURES; OVERFITTING; PENMAN-MONTEITH METHOD; RASHT CITY (IRAN); REFERENCE EVAPOTRANSPIRATION; TIME SERIES PREDICTION;

FORECASTING; MATHEMATICAL MODELS; NEURAL NETWORKS; WATER SUPPLY;

EVAPOTRANSPIRATION;

ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; ENSEMBLE FORECASTING; EVAPOTRANSPIRATION; NUMERICAL MODEL; PREDICTION;

GILAN; IRAN; RASHT;

EID: 84880082142     PISSN: 14363240     EISSN: 14363259     Source Type: Journal    
DOI: 10.1007/s00477-012-0678-6     Document Type: Article

References (62)

1. Adeli H, Hung SL (1995) Machine learning-neural networks, genetic algorithms, and fuzzy systems. Wiley, New York.
2. Adeli H, Vishnubhotla PR (1992) Parallel processing and parallel machines. In: Adeli H (ed) Parallel processing in computational mechanics. Marcel Dekker, New York, pp 1-20.
3. Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration: guidelines for computing crop water requirements. FAO irrigation and drainage paper 56, Food and Agricultural Organization of the United Nations (FAO), Rome. Artificial Neural Network. J Irrig Drain Eng 129(6): 454-457.
4. Awchi TA (2008) Application of radial basis function neural networks for reference evapotranspiration prediction. Al-Rafi-dain Eng 16(1): 117-129.
5. Birkinshaw SJ, Parkin G, Rao Z (2008) A hybrid neural networks and numerical models approach for predicting groundwater abstraction impacts. J Hydroinformatics 10(2): 127-137.
6. Bishop CM (1996) Neural networks for pattern recognition, 1st edn. Oxford University Press, Oxford.
7. Boto-Giralda D, Díaz-Pernas FJ, González-Ortega D, Díez-Higuera JF, Antón-Rodríguez M, Martínez-Zarzuela M, Torre-Díez I (2010) Wavelet-based denoising for traffic volume time series forecasting with self-organizing neural networks. Comput Aided Civil Infrastruct Eng 25(7): 530-545.
8. Box GH, Jenkins G (1970) Time series analysis: Forecasting and control. San Francisco, Holden-Day.
9. Bulygina N, Gupta H (2010) How Bayesian data assimilation can be used to estimate the mathematical structure of a model. Stoch Environ Res Risk Assess 24: 925-937.
10. Chattopadhyay S, Chattopadhyay G (2008) Identification of the best hidden layer size for three layered neural net in predicting monsoon rainfall in India. J Hydroinformatics 10(2): 181-188.
11. Chauhan S, Shrivastava RK (2008) Performance evaluation of reference evapotranspiration estimation using climate based methods and artificial neural networks. Water Resour Manag 23: 825-837.
12. Chowdhury S, Sharma A (2009) Multisite seasonal forecast of arid river flows using a dynamic model combination approach. Water Resour Res 45(10): art. no. W10428.
13. Clair TA, Ehrman JM (1998) 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): 447-455.
14. Coulibaly P, Anctil F, Bobee B (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J Hydrol 230(3-4): 244-257.
15. Dibike BY, Coulibaly P (2008) TDNN with logical values for hydrologic modeling in a cold and snowy climate. J Hydroinformatics 10(4): 289-300.
16. Drucker H, Cortes C, Jackel D (1994) Boosting and other ensemble methods. Neural Comput 6: 1289-1301.
17. El-Shafie A, Abdin AE, Noureldin A, Taha MR (2009) Enhancing inflow forecasting model at Aswan high dam utilizing radial basis neural network and upstream monitoring stations measurements. Water Resour Manag 23(11): 2289-2315.
18. El-Shafie AH, El-Shafie A, El Mazoghi HG, Shehata A, Taha MR (2011) Artificial neural network technique for rainfall forecasting applied to Alexandria, Egypt. Int J Phys Sci 6(6): 1306-1316.
19. El-Shafie A, Jaafer O, Seyed A (2011) Adaptive neuro-fuzzy inference system based model for rainfall forecasting in Klang River, Malaysia. Int J Phys Sci 6(12): 2875-2888.
20. Elshorbagy A, Simonovic SP, Panu US (2000) Performance evaluation of artificial neural networks for runoff prediction. J Hydrol Eng 5(4): 424-427.
21. Fernando DA, Jayawardena AW (1998) Runoff forecasting using RBF networks with OLS algorithm. J Hydrol Eng 3(3): 203-209.
22. Fooladmand HR (2011) Evaluation of some equations for estimating evapotranspiration in the south of Iran. Arch Agron Soil Sci 57(7): 741-752.
23. Gao G, Chong-Yu X, Chen D, Singh VP (2012) Spatial and temporal characteristics of actual evapotranspiration over Haihe River basin in China. Stoch Environ Res Risk Assess 26: 655-669.
24. Gibson GJ, Cowan CFN (1990) On the decision regions of multilayer perceptrons. Proc IEEE 78(10): 1590-1594.
25. Giustolisi O, Laucelli D (2005) Improving generalization of artificial neural networks in rainfall-runoff modelling / Amélioration de la généralisation de réseaux de neurones artificiels pour la modélisation pluiedébit. Hydrol Sci J 50(3): 457.
26. Giustolisi O, Simeone V (2006) Optimal design of artificial neural networks by a multi-objective strategy: groundwater level predictions. Hydrol Sci J 51(3): 502-523.
27. Graf W, Freitag S, Kaliske M, Sickert J-U (2010) Recurrent neural networks for uncertain time-dependent structural behavior. Comput Aided Civil Infrastruct Eng 25(5): 322-323.
28. Hargreaves GH, Samani ZA (1985) Reference crop evapotranspiration from temperature. Appl Eng Agric 1(2): 96-99.
29. Haykin S (1994) Neural networks: A comprehensive foundation. Macmillan College Publishing Company, New York.
30. Hsu K, Gupta HV, Sorooshian S (1995) Artificial neural network modeling of the rainfall-runoff process. Water Resour Res 31(10): 2517-2530.
31. IPCC 2007 (2009) Summary for policymakers. In: Solomon S, Qin D, Jain M, Das A, Srivastava DK (eds) Climate change 2007. Application of ANN for reservoir inflow prediction and operation. J Water Resour Plan Manag 125(5): 263-271.
32. Jacquin AP, Shamseldin AY (2009) Review of the application of fuzzy inference systems in river flow forecasting. J Hydroinformatics 11(3-4): 202-210.
33. Jain SK, Nayak PC, Sudheer KP (2008) Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation. Hydrol Process 22(13): 2225-2234.
34. Johnson F, Sharma A (2010) A comparison of australian open water body evaporation trends for current and future climates estimated from class a evaporation pans and general circulation models. J Hydrometeorol 11(1): 105-121.
35. Khajehzadeh M, Taha MR, El-Shafie A, Eslami M (2011) Modified particle swarm optimization for optimum design of spread footing and retaining wall. J Zhejiang U: Science A 12(6): 415-427.
36. Khoob AR (2008) Comparative study of Hargreaves's and artificial neural network's methodologies in estimating reference evapotranspiration in a semiarid environment. Irrig Sci 26(3): 253-259.
37. Kisi O (2009) Modeling monthly evaporation using two different neural computing techniques. Irrig Sci 27: 417-430.
38. Kumar M, Raghuwanshi NS, Singh R, Wallender WW, Pruitt WO (2002) Estimating evapotranspiration using artificial neural network. J Irrig Drain Eng 128(4): 224-233.
39. Liong SY, Khu ST, Chan WT (2001) Derivation of Pareto front with genetic algorithm and neural network. J Hydrol Eng 6(1): 52-61.
40. Maier HR, Dandy GC (1999) Empirical comparison of various methods for training feed-forward neural networks for salinity forecasting. Water Resour Res 35(8): 2591-2596.
41. Noureldin A, El-Shafie A, Bayoumi M (2011) GPS/INS integration utilizing dynamic neural networks for vehicular navigation. Inf Fusion 12(1): 48-57.
42. Odhiambo LO, Yoder RE, Hines JW (2001) Optimization of fuzzy evapotranspiration model through neural training with input-output examples. Trans ASAE 44(6): 1625-1633.
43. Rahimi Khoob A, Behbahani MB, Fakheri J (2012) An evaluation of four reference evapotranspiration models in a subtropical climate. Water Resour Manag 26(10): 2867-2881.
44. Reuter U, Möller B (2010) Artificial neural networks for forecasting of fuzzy time series. Comput Aided Civil Infrastruct Eng 25(5): 363-374.
45. Ripley BD (1996) Pattern recognition and neural networks, Cambridge University Press, New York.
46. o prediction in coastal locations by means of preliminary models and exogenous data. In: 2008 eighth international conference on hybrid intelligent systems, pp 344-349.
47. Sabziparvar AA, Tabari H (2010) Regional estimation of reference evapotranspiration in arid and semi-arid regions. J Irrig Drain Eng ASCE 136(10): 724-731.
48. Samant A, Adeli H (2000) Feature extraction for traffic incident detection using wavelet transform and linear discriminant analysis. Comput Aided Civil Infrastruct Eng 15(4): 241-250.
49. Sudheer KP, Gosain AK, Ramasastri KS (2003) Estimating actual evapotranspiration from limited climatic data using neural computing technique. J Irrig Drain Eng 129(3): 214-218.
50. Tabari H (2010) Evaluation of reference crop evapotranspiration equations in various climates. Water Resour Manag 24: 2311-2337.
51. Tabari H, Grismer ME, Trajkovic S (2011) Comparative analysis of 31 reference evapotranspiration methods under humid conditions. Irrig Sci. doi: 10. 1007/s00271-011-0295.
52. Tabari H, Martinez C, Ezani A, Hosseinzadeh Talaee P (2012) Applicability of support vector machines and adaptive neurofuzzy inference system for potato crop evapotranspiration forecasting. Irrig Sci. doi: 10. 1007/s00271-012-0332-6.
53. Telesca L, Vicente-Serrano SM, Lo′pez-Moreno JI (2012) Power spectral characteristics of drought indices in the Ebro river basin at different temporal scales. Stoch Environ Res Risk Assess. doi: 10. 1007/s00477-012-0651-4.
54. Todorovic B, Stankovic M, Todorovic-Zakula S (2000) Structurally adaptive RBF network in nonstationary time series prediction. In: Proceedings of adaptive systems for signal processing, communication and control symposium, IEEE, New York, pp 224-229.
55. Tokar AS, Johnson PA (1999) Rainfall-runoff modeling using artificial neural networks. J Hydrol Eng 4(3): 232-239.
56. Trajkovic S, Todorovic B, Stankovic M (2003) Forecasting of reference evapotranspiration by artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian zone. Agric Water Manag 97(5): 707-714.
57. Tsanis IK, Coulibaly P, Daliakopoulos IN (2008) Improving groundwater level forecasting with a feed forward neural network and linearly regressed projected precipitation. J Hydroinformatics 10(4): 317-330.
58. Wang YM, Kerh T (2008) Neural network approach for estimating reference evapotranspiration from limited climatic data in Burkina Faso. WSEAS Trans Comput 7(6): 704-713.
59. Yang T, Chen X, Xu Chong-Yu, Zhang Z-C (2009) Spatio-temporal changes of hydrological processes and underlying driving forces in Guizhou region, Southwest China. Stoch Environ Res Risk Assess 23: 1071-1087.
60. Zanetti SS, Sousa EF, Oliveira VPS, Almeida FT, Bernardo S (2007) Estimating evapotranspiration using artificial neural network and minimum climatological data. J Irrig Drain Eng 133(2): 83-89.
61. Zamani A, Heemink A, Solomatine D (2009) Wave height prediction at the Caspian Sea using a data driven model and ensemble-based data assimilation methods. J Hydroinformatics 11(2): 154-164.
62. Zhang Q, Chong-Yu X, Chen YD, Ren L (2011) Comparison of evapotranspiration variations between the Yellow River and Pearl River basin, China. Stoch Environ Res Risk Assess 25: 139-150.