Tree Sap Flow Prediction Based on the Fusion of CEEMDAN-Copula Entropy-LSTM

© 2026 by the Authors. Licensee Whioce Publishing, Singapore. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )

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Abstract

Copula 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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