Regression Models for Predicting Reference Evapotranspiration
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.