Sap Flow Prediction of Larix olgensis based on Feature Extraction CNN-BiLSTM
[Objective]The stem sap flow density is a crucial factor affecting plant transpiration.Its magnitude is influenced by environmental variables such as solar radiation,air humidity,and soil moisture.Accurate measurement or estimation of stem sap flow density is of great significance for understanding forest water use efficiency and studying global climate change.[Method]In this paper,a feature extraction-based prediction method for sap flow in Larix olgensis is proposed.Firstly,Larix olgensis from Mengjiagang forest in Jiamusi city,Heilongjiang province,are selected as the experimental subjects,and data are collected from meteorological monitoring stations in the forest,including nine environmental variables and the sap flow of Larix olgensis measured by the Granier method,to construct the dataset.Secondly,the linear relationship between environmental variables and sap flow is analyzed using Pearson correlation coefficient method.Then,feature extraction is performed using transfer entropy method to extract four major environmental variables as inputs for the model.Finally,a CNN-BiLSTM hybrid model based on feature data is constructed,and the dataset is input into the model for training and testing.[Result]Through comparative experiments,with correlation coefficient,root mean square error,and mean absolute error as evaluation indicators of prediction accuracy,the CNN-BiLSTM method outperforms the prediction methods of BP,CNN,and CNN-LSTM models in terms of performance.[Conclusion]The transfer entropy method can effectively analyze the causal relationship between environmental variables and lagged introduction of Larix olgensis sap flow.The CNN-BiLSTM model constructed based on transfer entropy-based feature extraction can effectively improve the prediction accuracy of stem sap flow density in Larix olgensis.
Larix olgensisstem sap flow densitytransfer entropyCNN-BiLSTM hybrid model
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