Performance assessment of data mining techniques for Forecast for one year evaporation (A Case Study: Yazd synoptic station)

Evaporation is considered one of the key climatic variables, especially in arid regions and evaporation losses is one of the important issues in irrigation and water resources management in these areas. Therefore, it is important being aware of the amount of evaporation and its modeling, as one of the most important hydrological variables in agricultural research and water and soil conservation. In recent decades, artificial intelligence techniques have proven high capability and flexibility to estimate and predict nonlinear phenomena. In this study, three important data mining techniques including Artificial Neural Network, Active Neuro-Fuzzy Inference System and Regression Decision Tree were used for predicting evaporation. For this purpose, 8 climatic variables (Minimum average temperature, average maximum temperature, average temperature, sunshine hours, wind speed, wind direction, relative humidity and evaporation averages) were employed in this study. The results showed three models are able to predict evaporation for 12 months after. Finally among the used models, ANN showed better performance with coefficient efficiency of 0.97 and RMSE of 5.1and ME of 0.48. Also, The results showed that there is not significant difference in simulation results to predict the evaporation between two scenario, original data and normalized data.