Recognition of dominant driving factors behind sap flow of Liquidambar formosana based on back-propagation neural network method

Key message
Three-layered back-propagation (BP) neural networks with architecture 4–10-1 (four neurons in the input layer, ten neurons in the hidden layer and one neuron in the output layer) performed better than MLR (multivariate linear regression) in modelling complex nonlinear relationships between sap flow and driving factors. The optimum BP model was achieved with an input combination of air temperature, relative humidity, average net radiation, and a phenological index. The performance of BP models indicated a small improvement with the inclusion of a phenological index.

Abstract
Aims This study focused on the applicability of back-propagation (BP) neural networks in simulating sap flow (SF) using meteorological factors and a phenological index (PI) for Liquidambar formosana, a deciduous broad-leaf tree species in subtropical China, and thus providing a useful and promising alternative to traditional methods for transpiration prediction.
Methods Three-layered BP models with an architecture 4–10-1 (four neurons in the input layer, ten neurons in the hidden layer and one neuron in the output layer) were trained and tested using the Levenberg–Marquardt (LM) algorithm based on in situ observations of SF and concurrent microclimate at the Qianyanzhou Ecological Station, Jiangxi Province, Southeast China. The model performance was verified with testing data not used in model development.
Results The BP models with eight input combinations proved a satisfactory fit: the determination coefficients (R2) and fitting accuracies (Acc) (about 0.8 and 70%) were significantly higher than those of the multivariate linear regression (MLR) (about 0.5 and 50%), indicating their advantage in solving complex nonlinear relationships involved in transpiration. In addition, the BP models showed a bit better performance by adding PI to the input family. The best BP model was achieved taking air temperature (Ta), relative humidity (RH), average net radiation (ANR), and PI as the input and sap flux density (vs) as the output, with maximum R2 and Acc as high as 0.95 and 90%, respectively.
Conclusion The BP models with input combination of Ta, RH, ANR, and PI mirrored very well measured daily variations in vs. The results could be used to fine-tune estimations of sap flow by Liquidambar formosana, and thus shed light on the eco-hydrological process related to transpiration for deciduous broad-leaf trees.

Keywords
Liquidambar formosana; Sap flux density; Meteorological factors; Back-propagation neural network; Phenological index

Publication
Tu, J., Liu, Q. & Wu, J. Recognition of dominant driving factors behind sap flow of Liquidambar formosana based on back-propagation neural network method. Annals of Forest Science 78, 95 (2021). https://doi.org/10.1007/s13595-021-01091-y

For the read-only version of the full text:
https://rdcu.be/cBrSO

Data and/or Code availability
The datasets generated and/or analyzed during the current study are available in the Dryad repository, https://doi.org/10.5061/dryad.p5hqbzknx

Handling Editor
Barry A. Gardiner

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