Wind Shear Prediction from Light Detection and Ranging Data Using Machine Learning Methods
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 susta...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-05-01
|
| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/12/5/644 |
| _version_ | 1797533641250701312 |
|---|---|
| author | Jingyan Huang Michael Kwok Po Ng Pak Wai Chan |
| author_facet | Jingyan Huang Michael Kwok Po Ng Pak Wai Chan |
| author_sort | Jingyan Huang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T11:18:30Z |
| format | Article |
| id | doaj.art-d86c49a0c1844bbf9fb2ccde47055d85 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T11:18:30Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-d86c49a0c1844bbf9fb2ccde47055d852023-11-21T20:17:16ZengMDPI AGAtmosphere2073-44332021-05-0112564410.3390/atmos12050644Wind Shear Prediction from Light Detection and Ranging Data Using Machine Learning MethodsJingyan Huang0Michael Kwok Po Ng1Pak Wai Chan2Department of Statistics and Applied Probability, National University of Singapore, Singapore 119077, SingaporeDepartment of Mathematics, The University of Hong Kong, Hong Kong 999077, Hong KongAviation Weather Services, Hong Kong Observatory, Hong Kong 999077, Hong KongThe 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.https://www.mdpi.com/2073-4433/12/5/644machine learning methodslight detection and ranging dataprediction modelswind shear detection |
| spellingShingle | Jingyan Huang Michael Kwok Po Ng Pak Wai Chan Wind Shear Prediction from Light Detection and Ranging Data Using Machine Learning Methods Atmosphere machine learning methods light detection and ranging data prediction models wind shear detection |
| title | Wind Shear Prediction from Light Detection and Ranging Data Using Machine Learning Methods |
| title_full | Wind Shear Prediction from Light Detection and Ranging Data Using Machine Learning Methods |
| title_fullStr | Wind Shear Prediction from Light Detection and Ranging Data Using Machine Learning Methods |
| title_full_unstemmed | Wind Shear Prediction from Light Detection and Ranging Data Using Machine Learning Methods |
| title_short | Wind Shear Prediction from Light Detection and Ranging Data Using Machine Learning Methods |
| title_sort | wind shear prediction from light detection and ranging data using machine learning methods |
| topic | machine learning methods light detection and ranging data prediction models wind shear detection |
| url | https://www.mdpi.com/2073-4433/12/5/644 |
| work_keys_str_mv | AT jingyanhuang windshearpredictionfromlightdetectionandrangingdatausingmachinelearningmethods AT michaelkwokpong windshearpredictionfromlightdetectionandrangingdatausingmachinelearningmethods AT pakwaichan windshearpredictionfromlightdetectionandrangingdatausingmachinelearningmethods |