Computational deep air quality prediction techniques: a systematic review

Kaur, Manjit; Singh, Dilbag; Jabarulla, Mohamed Yaseen; Kumar, Vijay; Kang, Ju-Sung; Lee, Heung-No

doi:10.1007/s10462-023-10570-9

Cited by 3 publications

(1 citation statement)

References 185 publications

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“…In recent years, deep learning technology has developed rapidly, and it has been widely used in many research fields, such as lithium battery health prediction [16][17][18], bearing fault diagnosis [19][20][21][22], and air quality prediction [23][24][25][26]. Therefore, some researchers have started to study the anomaly detection of UAV flight data based on deep learning technology to ensure the safety and reliability of UAV flights.…”

Section: Introductionmentioning

confidence: 99%

An Anomaly Detection Method for UAV Based on Wavelet Decomposition and Stacked Denoising Autoencoder

Zhou,

He,

Chen

et al. 2024

Aerospace

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The paper proposes an anomaly detection method for UAVs based on wavelet decomposition and stacked denoising autoencoder. This method takes the negative impact of noisy data and the feature extraction capabilities of deep learning models into account. It aims to improve the accuracy of the proposed anomaly detection method with wavelet decomposition and stacked denoising autoencoder methods. Anomaly detection based on UAV flight data is an important method of UAV condition monitoring and potential abnormal state mining, which is an important means to reduce the risk of UAV flight accidents. However, the diversity of UAV mission scenarios leads to a complex and harsh environment, so the acquired data are affected by noise, which brings challenges to accurate anomaly detection based on UAV data. Firstly, we use wavelet decomposition to denoise the original data; then, we used the stacked denoising autoencoder to achieve feature extraction. Finally, the softmax classifier is used to realize the anomaly detection of UAV. The experimental results demonstrate that the proposed method still has good performance in the case of noisy data. Specifically, the Accuracy reaches 97.53%, the Precision is 97.50%, the Recall is 91.81%, and the F1-score is 94.57%. Furthermore, the proposed method outperforms the four comparison models with more outstanding performance. Therefore, it has significant potential in reducing UAV flight accidents and enhancing operational safety.

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