Daily Pan Evaporation Estimation Using Artificial Neural Network-based Models

International Journal Of Agricultural Science, Research And Technology In Extension And Education Systems

مقاله 3، دوره 1، شماره 4، اسفند 2011، صفحه 159-163   

نویسنده

Karimi-Googhari, Sh

Assistant Professor, Department of Water Engineering, Faculty of Agriculture, Shahid-Bahonar University of Kerman, Kerman, I.R. of Iran

چکیده

Accurate estimation of evaporation is important for design, planning and operation of water systems. In arid zones where water resources are scarce, the estimation of this loss becomes more interesting in the planning and management of irrigation practices. This paper investigates the ability of artificial neural networks (ANNs) technique to improve the accuracy of daily evaporation estimation. Four different ANNs model comprising various combinations of daily climatic variables, that is, air temperature, daily sunshine hours, wind speed, and relative humidity are developed to evaluate degree of effect of each mentioned variables on evaporation for two stations located in central part if I.R. of Iran. A comparison is made between the estimates provided by the ANNs model and the multiple linear regression models. Various statistic measures are used to evaluate the performance of the models. Based on the comparisons, it was revealed that the ANNs computing technique could be employed successfully in modeling of evaporation process from the available climatic data. The ANN also increased dramatically the accuracy of evaporation estimation compare to the multiple linear regression models. [SH, Karimi-Googhari. Daily Pan Evaporation Estimation Using Artificial Neural Network-based Models. International Journal of Agricultural Science, Research and Technology, 2011; 1(4):159-163].

کلیدواژه‌ها

Evaporation؛ Irrigation؛ Estimation؛ Neural Networks؛ arid region

مراجع

1. Castellano, M. M., González, M . W., Febrero, B. B., Prada, S. J. M and Lozano, C. R. (2004). Modeling of the monthly and daily behavior of the runoff of the Xallas River using Box–Jenkins and neural networks methods. Journal of Hydrology, 296: 38-55.

2. Gavin, H and Agnew, C. A. (2004). Modeling actual, reference and equilibrium evaporation from a temperate wet grassland. Hydrological Processes, 18: 229–246.

3. Hsu, K., Gupta, H. V and Sorooshian, S. (1995). Artificial neural network modeling of the rainfall-runoff process. Water Resources Research, 31(10):2517–2530.

4. Irmak, S., Haman, D and Jones, J. W. (2002). Evaluation of class ‘A’ pan coefficients for estimating reference evapotranspiration in a humid location. Journal of Irrigation and Drainage Engineering, ASCE 128(3): 153–159.

5. Karimi-Googhari, Sh and Lee T. S. (2011). Applicability of Adaptive Neuro-Fuzzy Inference Systems in Daily Reservoir Inflow Forecasting. International Journal of Soft Computing, 6: 75-84.

6. Khalil, M., Panu, U. S and Lennox, W. C. (2001). Groups and neural networks based stream flow data infilling procedures. Journal of Hydrology, 241:153–176.

7. Kisi, O. (2005). Daily river flow forecasting using artificial neural networks and auto-regressive models. Turkish Journal of Engineering and Environmental Sciences, 29: 9–20.

8. Kisi, O. (2006). Daily pan evaporation modelling using a neuro-fuzzy computing technique. Journal of Hydrology, 329: 636– 646.

9. Kumar, D. N., Raju, K. S and Sathish, T. (2004). River flow forecasting using recurrent neural networks. Water Resources Management, 18: 143–161.

10. Reis, R. J and Dias, N. L. (1998). Multi-season lake evaporation: energy budget estimates and CRLE model assessment with limited meteorological observations. Journal of Hydrology, 208: 135–147.

11. Stephens, J. C and Stewart, E. H. (1963). A comparison of procedures for computing evaporation and evapotranspiration. Publication 62, International Association of Scientific Hydrology. International Union of Geodynamics and Geophysics, Berkeley, CA, 123–133.

12. Sudheer, K. P., Gosain, A. K., Rangan, D. M and Saheb, S. M. (2002). Modeling evaporation using an artificial neural network algorithm. Hydrological Processes, 16: 3189–3202.

13. Supharatid, S. (2003). Application of a neural network model in establishing a stage–discharge relationship for a tidal river. Hydrological Processes, 17: 3085–3099.

14. Vos, N. J and Rientjes, T. H. (2005). Constraints of artificial neural networks for rainfall-runoff modeling: trade-offs in hydrological state representation and model evaluation. Hydrology and Earth System Sciences, 2: 365–415.

15. Zealand, C. M., Burn, D. H and Simonovic SP (1999). Short term streamflow forecasting using artificial neural networks. Journal of Hydrology, 214: 32–48.