Time-Series Prediction of Intense Wind Shear Using Machine Learning Algorithms: A Case Study of Hong Kong International Airport

被引:8
|
作者
Khattak, Afaq [1 ]
Chan, Pak-Wai [2 ]
Chen, Feng [1 ]
Peng, Haorong [3 ]
机构
[1] Tongji Univ, Key Lab Infrastructure Durabil & Operat Safety Air, 4800 Caoan Rd, Shanghai 201804, Peoples R China
[2] Hong Kong Observ, 134A Nathan Rd, Hong Kong, Peoples R China
[3] Shanghai Res Ctr Smart Mobil & Rd Safety, Shanghai 200092, Peoples R China
基金
中国国家自然科学基金;
关键词
wind shear; time-series modeling; machine learning; Bayesian optimization; LIDAR;
D O I
10.3390/atmos14020268
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Machine learning algorithms are applied to predict intense wind shear from the Doppler LiDAR data located at the Hong Kong International Airport. Forecasting intense wind shear in the vicinity of airport runways is vital in order to make intelligent management and timely flight operation decisions. To predict the time series of intense wind shear, Bayesian optimized machine learning models such as adaptive boosting, light gradient boosting machine, categorical boosting, extreme gradient boosting, random forest, and natural gradient boosting are developed in this study. The time-series prediction describes a model that predicts future values based on past values. Based on the testing set, the Bayesian optimized-Extreme Gradient Boosting (XGBoost) model outperformed the other models in terms of mean absolute error (1.764), mean squared error (5.611), root mean squared error (2.368), and R-Square (0.859). Afterwards, the XGBoost model is interpreted using the SHapley Additive exPlanations (SHAP) method. The XGBoost-based importance and SHAP method reveal that the month of the year and the encounter location of the most intense wind shear were the most influential features. August is more likely to have a high number of intense wind-shear events. The majority of the intense wind-shear events occurred on the runway and within one nautical mile of the departure end of the runway.
引用
收藏
页数:14
相关论文
共 50 条
  • [1] Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil
    Khosravi, A.
    Machado, L.
    Nunes, R. O.
    [J]. APPLIED ENERGY, 2018, 224 : 550 - 566
  • [2] Severe wind shear at Hong Kong International Airport: climatology and case studies
    Chan, P. W.
    [J]. METEOROLOGICAL APPLICATIONS, 2017, 24 (03) : 397 - 403
  • [3] MACHINE LEARNING ALGORITHMS FOR TIME-SERIES FORECASTINGRAINFALL PREDICTION
    Regulagadda, Rama Krishna
    Kumar, P. Om Sai
    Yamini, P.
    Niharika, K.
    Madhavi, Kilaru
    [J]. INTERNATIONAL JOURNAL OF EARLY CHILDHOOD SPECIAL EDUCATION, 2022, 14 (04) : 1328 - 1338
  • [4] Modelling of wind shear downwind of mountain ridges at Hong Kong International Airport
    Carruthers, David
    Ellis, Andrew
    Hunt, Julian
    Chan, P. W.
    [J]. METEOROLOGICAL APPLICATIONS, 2014, 21 (01) : 94 - 104
  • [5] A significant wind shear event leading to aircraft diversion at the Hong Kong international airport
    Chan, P. W.
    [J]. METEOROLOGICAL APPLICATIONS, 2012, 19 (01) : 10 - 16
  • [6] Prediction of hydrological time-series using extreme learning machine
    Atiquzzaman, Md
    Kandasamy, Jaya
    [J]. JOURNAL OF HYDROINFORMATICS, 2016, 18 (02) : 345 - 353
  • [7] Prediction of water turbidity in a marine environment using machine learning: A case study of Hong Kong
    Kumar, Lalit
    Afzal, Mohammad Saud
    Ahmad, Ashad
    [J]. REGIONAL STUDIES IN MARINE SCIENCE, 2022, 52
  • [8] Time-series failure prediction on small datasets using machine learning
    Maior, Caio B. S.
    Silva, Thaylon G.
    [J]. IEEE LATIN AMERICA TRANSACTIONS, 2024, 22 (05) : 362 - 371
  • [9] A Time-Series Approach for Shock Outcome Prediction Using Machine Learning
    Shandilya, Sharad
    Ward, Kevin R.
    Najarian, Kayvan
    [J]. 2010 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS (BIBMW), 2010, : 440 - 446
  • [10] Time-series prediction of organomineral fertilizer moisture using machine learning
    Korkmaz, Cem
    Kacar, Ilyas
    [J]. APPLIED SOFT COMPUTING, 2024, 165