JERAJournal of Electronic Research and Application2208-35022208-3510Bio-Byword Scientific Publishing Pty. Ltd.10.26689/jera.v10i4.14909ArticleTree Sap Flow Prediction Based on the Fusion of CEEMDAN-Copula Entropy-LSTMhttps://artdesignp.com/journal/JERA/10/4/10.26689/jera.v10i4.14909WangZixiang,XuKai20261042026-05-21Copula entropyTree trunk sap flow is jointly affected by environmental factors and physiological mechanisms, showing nonlinear and random characteristics, which makes it difficult for traditional methods to achieve high-precision prediction. To address this problem, this paper introduces CEEMDAN to decompose the sap flow sequence at multiple scales, combines Copula entropy and signal energy to construct a modal component reconstruction strategy, and further uses LSTM to realize prediction. Experimental results show that the proposed model achieves 0.6759 and 0.9755 in MAPE and R2 indicators respectively, which is superior to the comparison models, providing a new idea for sap flow prediction and transpiration flux estimation.

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