Artificial neural network models for estimating regional reference evapotranspiration based on climate factors.

The article focuses on the findings from a study that explored the use of artificial neural networks (ANN) model in estimating reference evapotranspiration (ET), an essential component of the hydrologic cycle and irrigation water requirements estimation, based on climatic factors as applied in some areas of the Inner Monglia region of China. ANN model was tested and trained for arid, semi-arid and sub-humid areas. The climatic elements considered in the study include air temperature (T), relative humidity (RH) and wind velocity (U). The implication of the results is also discussed.