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    冯新妍, 贾昕, 黄金泽, 高圣杰, 袁敏, 刘甜甜, 靳川. ANN-BiLSTM模型在温带荒漠灌丛碳通量数据缺失值插补中的应用[J]. 北京林业大学学报, 2023, 45(9): 62-72. DOI: 10.12171/j.1000-1522.20220510
    引用本文: 冯新妍, 贾昕, 黄金泽, 高圣杰, 袁敏, 刘甜甜, 靳川. ANN-BiLSTM模型在温带荒漠灌丛碳通量数据缺失值插补中的应用[J]. 北京林业大学学报, 2023, 45(9): 62-72. DOI: 10.12171/j.1000-1522.20220510
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    Feng Xinyan, Jia Xin, Huang Jinze, Gao Shengjie, Yuan Min, Liu Tiantian, Jin Chuan. Application of ANN-BiLSTM model to long-term gap-filling of carbon flux data in temperate desert shrub[J]. Journal of Beijing Forestry University, 2023, 45(9): 62-72. DOI: 10.12171/j.1000-1522.20220510
    Citation: Feng Xinyan, Jia Xin, Huang Jinze, Gao Shengjie, Yuan Min, Liu Tiantian, Jin Chuan. Application of ANN-BiLSTM model to long-term gap-filling of carbon flux data in temperate desert shrub[J]. Journal of Beijing Forestry University, 2023, 45(9): 62-72. DOI: 10.12171/j.1000-1522.20220510
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    ANN-BiLSTM模型在温带荒漠灌丛碳通量数据缺失值插补中的应用

    Application of ANN-BiLSTM model to long-term gap-filling of carbon flux data in temperate desert shrub

      摘要
    • 摘要:
        目的  为提高净生态系统碳交换量（NEE）在长期缺失下的插补精度，利用人工神经网络（ANN）和双向长短期记忆网络（Bi-LSTM）将NEE的环境因子和时序特征相结合，提出了ANN-BiLSTM模型。
        方法  以宁夏盐池观测站NEE数据及微气象数据为研究对象，通过随机剔除连续7、15、30、45和90 d的5类缺失情景来评估ANN-BiLSTM模型、随机森林（RF）、人工神经网络（ANN）、K最邻近（KNN）、支持向量回归（SVR）和边际分布采样法（MDS）在NEE长期缺失下的插值结果。
        结果  当NEE缺失天数≤30 d时，各模型的插值精度相对可靠，ANN-BiLSTM模型的插值精度最高，决定系数（R2）均值在0.48 ~ 0.56之间，均方根误差（RMSE）和平均绝对误差（MAE）分别在0.68 ~ 1.92 μmol/(m2·s)、0.45 ~ 1.30 μmol/(m2·s)之间。当数据缺失天数 ≥ 45 d时，MDS不能对缺失值进行处理，ANN-BiLSTM模型的插值精度明显高于机器学习模型，R2均值 > 0.45，RMSE和MAE分别在0.79 ~ 1.95 μmol/(m2·s)、0.50 ~ 1.32 μmol/(m2·s)之间。
        结论  当温带荒漠灌丛生态系统的NEE数据缺失长度 > 30 d时，建议应用ANN-BiLSTM模型对缺失数据进行插补，可以在一定程度上提高NEE长期插值结果的精度。

       

      Abstract:
        Objective  In order to improve the gap-filling accuracy of net ecosystem productivity (NEE) under long-term missing, this study used the artificial neural network (ANN) and bi-directional long short-term memory (Bi-LSTM) to combine the environmental factors and temporal characteristics of NEE, proposing the ANN-BiLSTM model.
        Method  This study took the NEE data and micro-meteorological data of Yanchi Observatory in Ningxia of northwestern China as the research object, and evaluated the gap-filling results of the ANN-BiLSTM model, random forest (RF), ANN, K-nearest neighbor (KNN), support vector regression (SVR) and marginal distribution sampling (MDS) under long-term absence of NEE by randomly eliminating five kinds of missing scenarios for 7, 15, 30, 45 and 90 d.
        Result  When the number of missing days was ≤ 30 d, the gap-filling accuracy of each model was relatively reliable. The ANBiLSTM model had the highest gap-filling accuracy. The mean coefficient of determination (R2) was 0.48−0.56. The root mean squares of errors (RMSE) and mean absolute error (MAE) were 0.68−1.92 μmol/(m2·s) and 0.45−1.30 μmol/(m2·s). When the missing data days were ≥ 45, MDS cannot process missing values. The gap-filling accuracy of ANN-BiLSTM model was significantly higher than machine learning. The mean value of R2 > 0.45, RMSE and MAE were 0.79−1.95 μmol/(m2·s) and 0.50−1.31 μmol/(m2·s).
        Conclusion  When the length of missing NEE data in temperate desert shrub ecosystems is > 30 d, we suggest to use ANN-BiLSTM to interpolate the missing data, which can improve the accuracy of long-term NEE gap-filling results to a certain extent.

       

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