中圖分類號：TP391 文獻標志碼：A DOI: 10.16157/j.issn.0258-7998.245138
中文引用格式： 李波，武斌. 基于CNN-BiLSTM-Attetion的銀杏液流預測模型及環(huán)境因子影響研究[J]. 電子技術應用，2024，50(9)：101-105.
英文引用格式： Li Bo，Wu Bin. Research of ginkgo sap flow prediction model based on CNN-BiLSTM-Attetion and the impact of environmental factors[J]. Application of Electronic Technique，2024，50(9)：101-105.

Research of ginkgo sap flow prediction model based on CNN-BiLSTM-Attetion and the impact of environmental factors

Abstract： Sap flow is subject to the combined effects of physiological activities and multiple environmental factors, and exhibits nonlinear and stochastic characteristics, which poses a challenge to the accuracy of prediction models. In this regard, the advantages of CNN convolutional layer, BiLSTM bidirectional network structure and attention mechanism are combined to extract the local features, long-term dependence and key information of sap flow sequences, respectively, and the CNN-BiLSTM-Attetion sap flow prediction model is constructed according to the self-test ginkgo sap flow data set. The model has the R2, MSE, and MAE of 0.977 3, 0.002 9, and 0.013 4, respectively, which are all improved in varying degrees compared with the CNN, BiLSTM, XGBoost, RNN and TCN. In addition, feature engineering is also used to rank the importance of environmental factors and analyze the response regularity of ginkgo sap flow to environmental factors at the beginning of the growing season, which provides a theoretical basis for irrigation and maintenance of ginkgo at the beginning of the growing season.

Key words : sap flow prediction model；CNN-BiLSTM-Attetion；environmental factors；early growing season