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Title: Wind shear prediction from light detection and ranging data using machine learning methods
Authors: Huang, Jingyan
Ng, Michael Kwok Po
Chan, Pak Wai
Keywords: Light detection and ranging data
Machine learning methods
Prediction models
Wind shear detection
Issue Date: 18-May-2021
Publisher: MDPI AG
Citation: Huang, Jingyan, Ng, Michael Kwok Po, Chan, Pak Wai (2021-05-18). Wind shear prediction from light detection and ranging data using machine learning methods. Atmosphere 12 (5) : 644. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
Abstract: The main aim of this paper is to propose a statistical indicator for wind shear prediction from Light Detection and Ranging (LIDAR) observational data. Accurate warning signal of wind shear is particularly important for aviation safety. The main challenges are that wind shear may result from a sustained change of the headwind and the possible velocity of wind shear may have a wide range. Traditionally, aviation models based on terrain-induced setting are used to detect wind shear phenomena. Different from traditional methods, we study a statistical indicator which is used to measure the variation of headwinds from multiple headwind profiles. Because the indicator value is nonnegative, a decision rule based on one-side normal distribution is employed to distinguish wind shear cases and non-wind shear cases. Experimental results based on real data sets obtained at Hong Kong International Airport runway are presented to demonstrate that the proposed indicator is quite effective. The prediction performance of the proposed method is better than that by the supervised learning methods (LDA, KNN, SVM, and logistic regression). This model would also provide more accurate warnings of wind shear for pilots and improve the performance of Wind shear and Turbulence Warning System. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Source Title: Atmosphere
ISSN: 2073-4433
DOI: 10.3390/atmos12050643
Rights: Attribution 4.0 International
Appears in Collections:Students Publications

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