Comparison performance of two models DRAINMOD and Artificial Neural Network (ANN) for the forecast of water table

Farm experiments are useful in knowing the drainage systems but they have considerable limitations including the inability to use them as prediction tools. Application of simulation models can cover these deficiencies but it is necessary to use the field data to evaluate the accuracy of the model. In this study, Artificial Neural Networks (ANN) and model DRAINMOD are used to predict water table.
For this purpose, field R9-11 of the Debal Khazaei sugarcane plantation is selectedand Input parameters of the models, including fluctuations in water table, the volume of irrigation water, drainage flow, Climatic data, Soil physical properties and Drainage system parameters were measured from November 2013 to October 2014. The results have showed that the artificial neural network method has a highest accuracy in predicting water table. So that the average RMSE between measured and simulation with Artificial Neural Networks and DRAINMOD obtained 0.02 and 16.8 respectively.