Regression Models for Predicting Reference Evapotranspiration

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2016 by IJETT Journal
Volume-38 Number-3
Year of Publication : 2016
Authors : N.Manikumari, G.Vinodhini
DOI :  10.14445/22315381/IJETT-V38P224

Citation 

N.Manikumari, G.Vinodhini"Regression Models for Predicting Reference Evapotranspiration", International Journal of Engineering Trends and Technology (IJETT), V38(3),134-139 August 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
The FAO-56 Penman-Monteith method is a universally adopted method for computing Reference crop Evapotranspiration (ET0) approved worldwide to arrive Crop Evapotranspiration. The main focus of this work is to model the Reference evapotranspiration by three different regression models including the Multi Linear Regression (MLR) and Support Vector Regression (SVR) models. The analysis was carried out based on the data collected in the command area of Veeranam tank system during the period 1987 –2008 in India. Veeranam tank is one of the major irrigation tanks that forms part of Cauvery basin, the largest river basin of Tamilnadu, India. The results were compared with Penman–Monteith (FAO-PM) to select the best model. The results show that all models provided a closer agreement with the calculated values for FAOPM (R2 > 0.98). However, the SVR model gives better estimates than the other two models for estimating Reference evapotranspiration (ET0). The results showed that both SVR models provided better Root Mean Square (RMSE), Mean Absolute Error (MAE) and Correlation coefficient (R2).

 References

[1] Cobaner, M. (2013). Reference evapotranspiration based on Class A pan evaporation via wavelet regression technique. Irrigation Science, 31(2), 119-134.
[2] Jadhav, P. B., Kadam, S. A., & Gorantiwar, S. D. (2015). Comparison of methods for estimating reference evapotranspiration for Rahuri region. Journal of Agrometeorology, 17(2), 204.
[3] Jhajharia, D., Dinpashoh, Y., Kahya, E., Singh, V. P., & Fakheri?Fard, A. (2012). Trends in reference evapotranspiration in the humid region of northeast India. Hydrological Processes, 26(3), 421-435.
[4] Kumar, M., Raghuwanshi, N. S., Singh, R., Wallender, W. W., & Pruitt, W. O. (2002). Estimating evapotranspiration using artificial neural network. Journal of Irrigation and Drainage Engineering, 128(4), 224-233.
[5] Medeiros, P. V., Marcuzzo, F. F. N., Youlton, C., & Wendland, E. (2012). Error Autocorrelation and Linear Regression for Temperature?Based Evapotranspiration Estimates Improvement1. JAWRA Journal of the American Water Resources Association, 48(2), 297-305.
[6] Silva, H. J. F., Santos, M. S., Cabral Junior, J. B., & Spyrides, M. H. C. (2016). Modeling of reference evapotranspiration by multiple linear regression. Journal of Hyperspectral Remote Sensing, 6(1), 44-58.
[7] Tabari, H., Marofi, S., & Sabziparvar, A. A. (2010). Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression. Irrigation Science, 28(5), 399-406.
[8] Vicente-Serrano, S. M., Van der Schrier, G., Beguería, S., Azorin-Molina, C., & Lopez-Moreno, J. I. (2015). Contribution of precipitation and reference evapotranspiration to drought indices under different climates. Journal of Hydrology, 526, 42-54.

Keywords
water; irrigation; regression; evapotranspiration.