Simulation of artificial neural network model for trunk sap flow of Pyrus pyrifolia and its comparison with multiple-linear regression

Abstract

Trunk sap flow of tree is an important index in the irrigation decision of orchard. On the basis of the measured sap flow (SF) of pear tree ( Pyrus pyrifolia) in the field, the multiple-linear regression for simulating the SF was obtained after analyzing the relationships between the SF and its affecting factors in this study and an artificial neural network (ANN) technique was applied to construct a nonlinear mapping to simulate the SF, then the simulated SF by two models was, respectively, compared to the measured value. Results showed that trunk SF had significant relationship with the vapour pressure deficit (VPD) in the single-variable analysis method but with soil volumetric water content ( θ) using the ANN models with default of different variables. The correlation coefficient ( R 2), mean relative error (MRE) and root mean square error (RMSE) between the measured and simulated sap flows by the ANN model developed by taking VPD, solar radiation ( S r), air temperature ( T), wind speed ( W s), θ, leaf area index (LAI) as the input variables were 0.953, 10.0% and 5.33 L d −1, respectively, and the simulation precision of ANN model was superior to that of multiple-linear regression due to its better performance for the nonlinear relationship between trunk SF and its affecting factors, thus ANN model can simulate trunk sap flow and then may help the efficient water management of orchard.