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- W2391183176 abstract "In Ludong University, the trunk sap flow of Sophora japonica f. pendula and the micro-environment were measured with AZ-M sap flow system made in USA and AXWG03 auto-weather station made in Australia. The applicability of BP-artificial neural network model in the quantitative analysis for trunk sap flow and environmental factors were conducted, and the fitting precision changes of BP-artificial neural network model before and after considering the effect of liquid-phase on lagged effect of micro-environment of meteorological factor were compared. The results show that the sap flow velocity relative to total solar radiation, photosynthesis available radiation and wind speed had sap flow lagging time of 10 min, 10 min and 20 min intervals, respectively; The fitting precision of BP-artifical neural network model considering time lag effect was improved significantly between environmental factors and trunk sap flow compared with traditional multiple linear regression model; The determining coefficients of the regressive equation between fitted value got from artificial neural network model with time lag effect considered or not and multiple linear regression model and measured value were 0.936, 0.866 and 0.831, respectively; The relative error between fitted value and measured value of sap flow velocity were 41.177%, 35.849%, 30.189% and 70.588%, 62.264%, 31.527% between ±5% and ±10%. The model fitting precision would be improved significantly considered with the time lag effect." @default.
- W2391183176 created "2016-06-24" @default.
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- W2391183176 date "2013-01-01" @default.
- W2391183176 modified "2023-09-25" @default.
- W2391183176 title "Artificial neural network model for stem sap flow of Chinese pagoda tree considering effect of time delay" @default.
- W2391183176 hasPublicationYear "2013" @default.
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