Deep learning coupled model based on TCN-LSTM for particulate matter concentration prediction

Ren, Ying; Wang, Siyuan; Xia, Biesheng

doi:10.1016/j.apr.2023.101703

Cited by 10 publications

(1 citation statement)

References 53 publications

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“…Considering the sequential flight trajectory data tp 1 , tp 2 , • • • , tp t , the feature extraction for the historical flight trajectory consisting of the preprocessed trajectory points is executed by utilizing the TCN network. The TCN network often favors the processing of sequential data, and thus the TCN is not susceptible to gradient vanishing and explosion problems when dealing with TP tasks [29].…”

Section: Feature Extraction Of the Trajectory Datamentioning

confidence: 99%

TCN-Informer-Based Flight Trajectory Prediction for Aircraft in the Approach Phase

Dong,

Fan,

et al. 2023

Sustainability

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Trajectory prediction (TP) is a vital operation in air traffic control systems for flight monitoring and tracking. The approach phase of general aviation (GA) aircraft is more of a visual approach, which is related to the safety of the flight and whether to go around. Therefore, it is important to accurately predict the flight trajectory of the approach phase. Based on the historical flight trajectories of GA aircraft, a TP model is proposed with deep learning after feature extraction in this study, and the hybrid model combines a time convolution network and an improved transformer model. First, feature extraction of the spatiotemporal dimension is performed on the preprocessed flight data by using TCN; then, the extracted features are executed by adopting the Informer model for TP. The performance of the novel architecture is verified by experiments based on real flight trajectory data. The results show that the proposed TCN-Informer architecture performs better according to various evaluation metrics, which means that the prediction accuracies of the hybrid model are better than those of the typical prediction models widely used today. Moreover, it has been verified that the proposed method can provide valuable suggestions for decision-making regarding whether to go around during the approach.

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Section: Feature Extraction Of the Trajectory Datamentioning

confidence: 99%