Hargreaves Method Improves Accuracy in Estimating Reference Evapotranspiration Adjustment Weight With the Help of Artificial Neural Network and Decision Tree

One of the most important components of the hydrological cycle is evapotranspiration which plays an important role in water resource management. In the present study the accuracy of evapotranspiration estimation by Hargreaves method and correction factor K was improved using the neural network and decision tree model M5. This coefficient is the ratio of Penman-Monteith evapotranspiration model is the method of Hargreaves. The data used in this study are the maximum and minimum temperatures and relative humidity in the period 2004-2013 Farokhshahr stations and airports in the region ShahrKord is cold and arid. Network Levenberg-Marquardt training algorithm is designed with a feedforward network and sigmoid tangent function is hidden in layers. Decision tree model was designed to help software WEKA. The results show that the neural network and decision tree model to model good performance, but the performance of the neural network model is more accurate correction factor. The results showed that the correction factor carefully before using the Hargreaves RMSE = 0.90 (Root Mean Square Error) Penman-Monteith than that this value after the correction factor to help RMSE = 0.69 and with the use of neural networks the correction factor to help decision tree to reach RMSE = 0.72. The results showed that after using a correction factor to the improved performance of Hargreaves.