Sap flow simulation of Cunninghamia lanceolata in degraded red soil region based on back propagation neural network
Cunninghamia lanceolata is commonly considered to be one of the most important tree species for forest restoration and reconstruction in subtropical area of China, owing to its advantages of rapid growth, good quality and high yield per unit area. However, they also consume certain amount of water d...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Zhejiang University Press
2015-03-01
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| Series: | 浙江大学学报. 农业与生命科学版 |
| Subjects: | |
| Online Access: | https://www.academax.com/doi/10.3785/j.issn.1008-9209.2014.05.191 |
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| Summary: | Cunninghamia lanceolata is commonly considered to be one of the most important tree species for forest restoration and reconstruction in subtropical area of China, owing to its advantages of rapid growth, good quality and high yield per unit area. However, they also consume certain amount of water during the course of growth and play roles of ecological benefits. Therefore, quantitative research on tree water consumption characteristics by transpiration has become a hot issue in the field of tree physiological ecology in recent years.Taking the C. lanceolata plantation in degraded red soil of Jiangxi Province as the research object, the log-sigmoid type function (tansig) of MATLAB toolbox was selected as the transmission function for the role of neurons. Four main factors including air temperature, relative air humidity, average net radiation and vapor pressure deficit were chosen as the input variables, and the sap flow velocity was selected as the output variable, to train and examine the neural network model with Bayesian regularization algorithm and Levenberg-Marquardt algorithm. The optimum network model of C. lanceolata sap flow velocity was built with the topological structure of 4-10-1.Based on Bayesian regularization algorithm and Levenberg-Marquardt algorithm, good fitting results were obtained from the linear regression between predictive and measured values, with correlation coefficients both higher than 0.93. The fitting accuracies of training samples were 83.57% and 83.06%, and the simulation accuracies of testing samples were 82.87% and 82.15%, respectively.In conclusion, the BP network model can well reflect the non-linear relationship between the meteorological factors and the sap flow velocity, thus may provide an effective tool for sustainable developing strategy of C. lanceolata plantations and scientific management of the associated water resource in the future. |
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| ISSN: | 1008-9209 2097-5155 |