Estimation of Monthly Mean Reference Evapotranspiration by Using Artificial Neural Network Models in Basrah City, South of Iraq


The main objective of this study is to evaluate the comparative performance of three artificial neural network techniques (radial basis functions “RBF”, multilayer perceptron “MLP”, and group method of data handling “GMDH”) based approach with the Penman–Monteith “PM” method for determining the group reference evapotranspiration “ET0” on monthly basis in Basrah City, south of Iraq. Climate information extends over 22 years (1991- 2012), monthly records of maximum temperature (Tmax), mean temperature (Tmean), minimum temperature (Tmin), wind speed (U) and relative humidity (RH) are used in this research. The architecture of artificial neural network models is performed during the process of training. The efficiency of trained model is checked by using the testing data, which is not used in the process of training. The evaluating of the artificial neural model performance is carried out by using cross-validation, a set of rows for each validation fold is determined randomly after stratification on the target variable “ET0”. Various set of climate inputs variables are used for creating nine artificial neural network models. The efficiency of artificial neural network models with two predictor variables (Tmean & U) for simulating ET0 is highly efficient according to the evaluation criteria. There is a significant improvement in the results of all artificial neural network models when using three input combination variables (Tmean, U, & RH) compared with the models that have only two-climate variables. Artificial neural network models especially (RBF, MLP, and GMDH) are efficient and powerful techniques for simulating ET0.