| Citation: | XING Z W，SUN K，LUO Q，et al. Imputation algorithm for flight ground support data based on graph neural network[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（5）：1528-1538 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0300
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A data imputation algorithm based on a graph neural network is proposed to address the issue of missing flight ground support data. Firstly, to reduce the impact of noise in the original data on training denoising autoencoder is applied to enhance the reliability of feature extraction. Secondly, a graph representation learning framework is established to get the first embedding, using aggregation functions to aggregate the features of nodes within the sampling interval to achieve state updating. Furthermore, a long and short-term memory neural network is constructed to embed the temporal feature of flights to obtain the final state space of the hidden layer. Lastly, a loss function is suggested to iterate the deconvolution neural network, which is employed for feature restoration. The final flight ground operation data imputation result was acquired after numerous iterations, and the technique was evaluated using ground operation data from a specific airport in Southwest China from April to June 2018. The results showed that compared to other algorithms, the proposed algorithm imputation error decreased by an average of about 74% at low missing rates. At higher missing rates, the imputation proposed algorithm error decreased by an average of about 68%. When the number of iterations of the proposed algorithm is about 100 and the regularization coefficient is about 0.5, the imputation error reaches the lowest.
| [1] |
SCHMIDT M. A review of aircraft turnaround operations and simulations[J]. Progress in Aerospace Sciences, 2017, 92: 25-38. doi: 10.1016/j.paerosci.2017.05.002
|
| [2] |
DU J H, HU M H, ZHANG W N. Missing data problem in the monitoring system: a review[J]. IEEE Sensors Journal, 2020, 20(23): 13984-13998. doi: 10.1109/JSEN.2020.3009265
|
| [3] |
JUNNINEN H, NISKA H, TUPPURAINEN K, et al. Methods for imputation of missing values in air quality data sets[J]. Atmospheric Environment, 2004, 38(18): 2895-2907. doi: 10.1016/j.atmosenv.2004.02.026
|
| [4] |
HAWTHORNE G, HAWTHORNE G, ELLIOTT P. Imputing cross-sectional missing data: comparison of common techniques[J]. Australian and New Zealand Journal of Psychiatry, 2005, 39(7): 583-590. doi: 10.1080/j.1440-1614.2005.01630.x
|
| [5] |
BERTSIMAS D, PAWLOWSKI C, ZHUO Y D. From predictive methods to missing data imputation: an optimization approach[J]. The Journal of Machine Learning Research, 2017, 18(1): 7133-7171.
|
| [6] |
RUBIN D B. An overview of multiple imputation[C]//Proceedings of the Survey Research Methods Section of the American Statistical Association. Princeton: Citeseer, 1988.
|
| [7] |
BELLO A L. Imputation techniques in regression analysis: looking closely at their implementation[J]. Computational Statistics & Data Analysis, 1995, 20(1): 45-57.
|
| [8] |
DEMPSTER A P, LAIRD N M, RUBIN D B. Maximum likelihood from incomplete data via the EM algorithm[J]. Journal of the Royal Statistical Society: Series B (Methodological), 1977, 39(1): 1-22. doi: 10.1111/j.2517-6161.1977.tb01600.x
|
| [9] |
COVER T, HART P. Nearest neighbor pattern classification[J]. IEEE Transactions on Information Theory, 1967, 13(1): 21-27. doi: 10.1109/TIT.1967.1053964
|
| [10] |
HUGHES R A, HERON J, STERNE J A C, et al. Accounting for missing data in statistical analyses: multiple imputation is not always the answer[J]. International Journal of Epidemiology, 2019, 48(4): 1294-1304. doi: 10.1093/ije/dyz032
|
| [11] |
CHEN X Y, HE Z C, SUN L J. A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation[J]. Transportation Research Part C: Emerging Technologies, 2019, 98: 73-84. doi: 10.1016/j.trc.2018.11.003
|
| [12] |
曾小华, 宋美洁, 宋大凤, 等. 基于车联网信息的公交客车行驶工况数据处理方法[J]. 吉林大学学报(工学版), 2021, 51(5): 1692-1699.
ZENG X H, SONG M J, SONG D F, et al. Data processing method of bus driving cycle based on vehicular network information[J]. Journal of Jilin University (Engineering and Technology Edition), 2021, 51(5): 1692-1699(in Chinese).
|
| [13] |
KUPPANNAGARI S R, FU Y, CHUENG C M, et al. Spatio-temporal missing data imputation for smart power grids[C]//Proceedings of the Twelfth ACM International Conference on Future Energy Systems. New York: ACM, 2021: 458-465.
|
| [14] |
余嘉茵, 何玉林, 崔来中, 等. 针对大规模数据的分布一致缺失值插补算法[J]. 清华大学学报(自然科学版), 2023, 63(5): 740-753.
YU J Y, HE Y L, CUI L Z, et al. Distribution consistency-based missing value imputation algorithm for large-scale data sets[J]. Journal of Tsinghua University (Science and Technology), 2023, 63(5): 740-753(in Chinese).
|
| [15] |
WANG Y X, LI K, GAN S J, et al. Missing data imputation with OLS-based autoencoder for intelligent manufacturing[J]. IEEE Transactions on Industry Applications, 2019, 55(6): 7219-7229. doi: 10.1109/TIA.2019.2940585
|
| [16] |
CUI Z Y, LIN L F, PU Z Y, et al. Graph Markov network for traffic forecasting with missing data[J]. Transportation Research Part C: Emerging Technologies, 2020, 117: 102671. doi: 10.1016/j.trc.2020.102671
|
| [17] |
YAO X, GAO Y, ZHU D, et al. Spatial origin-destination flow imputation using graph convolutional networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(12): 7474-7484. doi: 10.1109/TITS.2020.3003310
|
| [18] |
YANG J M, PENG Z R, LIN L. Real-time spatiotemporal prediction and imputation of traffic status based on LSTM and Graph Laplacian regularized matrix factorization[J]. Transportation Research Part C: Emerging Technologies, 2021, 129: 103228. doi: 10.1016/j.trc.2021.103228
|
| [19] |
CINI A, MARISCA I, ALIPPI C. Filling the g_ap_s: multivariate time series imputation by graph neural networks[EB/OL]. (2022-02-10)[2023-05-08]. http://arxiv.org/abs/2108.00298v3.
|
| [20] |
YOU J X, MA X B, DING D Y, et al. Handling missing data with graph representation learning[C]//Proceedings of the 34th International Conference on Neural Information Processing Systems. New York: ACM, 2020: 19075-19087.
|
| [21] |
陈小波, 陈程, 陈蕾, 等. 基于改进低秩矩阵补全的交通量数据缺失值插补方法[J]. 交通运输工程学报, 2019, 19(5): 180-190. doi: 10.3969/j.issn.1671-1637.2019.05.019
CHEN X B, CHEN C, CHEN L, et al. Interpolation method of traffic volume missing data based on improved low-rank matrix completion[J]. Journal of Traffic and Transportation Engineering, 2019, 19(5): 180-190(in Chinese). doi: 10.3969/j.issn.1671-1637.2019.05.019
|
| [22] |
张波, 宋国君. 大规模空气质量监测数据缺失处理方法实证研究[J]. 中国环境科学, 2022, 42(5): 2078-2087. doi: 10.3969/j.issn.1000-6923.2022.05.011
ZHANG B, SONG G J. Research on the missing value methods for large-scale online air quality monitoring data[J]. China Environmental Science, 2022, 42(5): 2078-2087(in Chinese). doi: 10.3969/j.issn.1000-6923.2022.05.011
|
| [23] |
陈帅, 赵明, 郭栋, 等. 基于SVD-KDR算法的工业监测数据插补技术[J]. 机械工程学报, 2021, 57(2): 30-38. doi: 10.3901/JME.2021.02.030
CHEN S, ZHAO M, GUO D, et al. Missing data imputation using SVD-KDR algorithm in industrial monitoring data[J]. Journal of Mechanical Engineering, 2021, 57(2): 30-38(in Chinese). doi: 10.3901/JME.2021.02.030
|
| [24] |
CHE Z P, PURUSHOTHAM S, CHO K, et al. Recurrent neural networks for multivariate time series with missing values[J]. Scientific Reports, 2018, 8(1): 6085. doi: 10.1038/s41598-018-24271-9
|
| [25] |
TIAN Y, ZHANG K L, LI J Y, et al. LSTM-based traffic flow prediction with missing data[J]. Neurocomputing, 2018, 318: 297-305. doi: 10.1016/j.neucom.2018.08.067
|
| [26] |
LUO Y H, CAI X R, ZHANG Y, et al. Multivariate time series imputation with generative adversarial networks[C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems. New York: ACM, 2018: 1603-1614.
|
| [27] |
SPINELLI I, SCARDAPANE S, UNCINI A. Missing data imputation with adversarially-trained graph convolutional networks[J]. Neural Networks, 2020, 129: 249-260. doi: 10.1016/j.neunet.2020.06.005
|
| [28] |
GUO Z J, WAN Y M, YE H. A data imputation method for multivariate time series based on generative adversarial network[J]. Neurocomputing, 2019, 360: 185-197. doi: 10.1016/j.neucom.2019.06.007
|
| [29] |
CHEN Y Y, LV Y S, WANG F Y. Traffic flow imputation using parallel data and generative adversarial networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(4): 1624-1630. doi: 10.1109/TITS.2019.2910295
|
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