Evapotranspiration Estimation using Six Different Multi-layer Perceptron Algorithms

Abstract

Evapotranspiration has a vital importance in water resources planning and management. In this study, the applicability of six different multi-layer perceptron (MLP) algorithms, Quasi-Newton, Conjugate Gradient, Levenberg- Marquardt, One Step Secant, Resilient Back propagation and Scaled Conjugate Gradient algorithms, in modeling reference evapotranspiration (ET0) is investigated. Daily climatic data of solar radiation, air temperature, relative humidity and wind speed from Antalya City are used as inputs to the MLP models to estimate daily ET0 values obtained using FAO 56 Penman Monteith empirical method. The results of the MLP algorithms are compared with those of the multiple linear regression models with respect to root mean square error (RMSE), mean absolute error (MAE), Willmott index of agreement (d) and determination coefficient (R2). The comparison results indicate that the Levenberg-Marquardt is faster and has a better accuracy than the other five training algorithms in modeling ET0. The Levenberg-Marquardt with RMSE = 0.083 mm, MAE = 0.006 mm, d = 0.999 and R2 = 0.999 in test period was found to be superior in modeling daily ET0 than the other algorithms, respectively.