Prediction of winter wheat evapotranspiration based on BP neural networks

By adopting meteorological data and the data from 2003 to 2006 collected from large weighing lysimeter with the crop of winter wheat at Yucheng Comprehensive Experimental Station, Chinese Academy of Sciences, a predicted model for winter wheat evapotranspiration was developed. Based on BP neural network, the model performance was tested with inputs of daily maximum temperature, net radiation, soil water content of top 60 cm layer, date number and measured crop coefficient and output of observed evaportranspiration. The topology of the neural network was 5-9-1 and the training function was Trainbr. The results showed that the model was good in simulating water consumption of winter wheat with average relative error of 13.1%, standard error of 0.88 mm, and Nash-Sutcliffe efficiency coefficient of 0.865. And the model can meet the requiements of production.