Crop evapotranspiration prediction by considering dynamic change of crop coefficient and the precipitation effect in back-propagation neural network model

Accurate prediction of crop evapotranspiration (ETc) can provide a scientific basis for improving water use efficiency, rational allocation of water resources, and sustainable management of ecosystems. However, conventional statistical methods have the limited application in some regions, because of the complex relationships (nonlinear, linear, exponential, etc) between ETc and its each driving factor. This paper illustrated the utility of the back-propagation neural network (BP) model for ETc prediction and compared the performance of this model with that of the multiple linear regression technique. Combined with the ETc measured by Eddy Covariance and the meteorological data, three-layer BP models, trained by the Levenberg-Marquardt algorithm, were developed with a 4-10-1 architecture (corresponding to four, ten, and one nodes in the input, hidden, and output layers, respectively). The BP models were trained and validated in the MATLAB environment, with three different combinations of maximum air temperature, minimum air temperature, sunshine hours, crop coefficient (K-c), and precipitation. A high correlation was observed between the values measured by Eddy Covariance and those predicted by BP, with a higher coefficient of determination (0.87) and accuracy (91.44%) than that achieved by the multiple linear regression model (0.79 and 82.96%, respectively). Furthermore, the performance of the BP models could be improved substantially by including the dynamic change of Kc and the effect of precipitation, which suggested that these two factors were crucial variables in modeling ETc by BP approaches. In testing sets, the BP model that considered the dynamic change of K-c was found to be superior to the BP model that considered the crop coefficient recommended by FAO-56, with prediction accuracy increased by 19%. Additionally, the accuracy of the BP prediction models that considered the dynamic change of K-c and the effect of precipitation were higher than 95%. Moreover, the BP prediction model in typical weather with precipitation was superior to the BP model in typical weather with no precipitation. The results obtained would be helpful for obtaining the ETc more consistent with the actual growth status of the crop, thereby providing a scientific basis for improving water management during crop production.

Automated summary

The paper discusses the application of the back-propagation neural network (BP) model in predicting crop evapotranspiration (ETc), showing superior performance compared to multiple linear regression techniques, especially when considering the dynamic change of crop coefficient (Kc) and the effect of precipitation. The results provide valuable insights for improving water management during crop production.