A New Frontier in Wind Shear Intensity Forecasting: Stacked Temporal Convolutional Networks and Tree-Based Models Framework
Wind shear presents a considerable hazard to aviation safety, especially during the critical phases of takeoff and landing. Accurate forecasting of wind shear events is essential to mitigate these risks and improve both flight safety and operational efficiency. This paper introduces a hybrid Tempora...
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MDPI AG
2024-11-01
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| author | Afaq Khattak Jianping Zhang Pak-wai Chan Feng Chen Abdulrazak H. Almaliki |
| author_facet | Afaq Khattak Jianping Zhang Pak-wai Chan Feng Chen Abdulrazak H. Almaliki |
| author_sort | Afaq Khattak |
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| description | Wind shear presents a considerable hazard to aviation safety, especially during the critical phases of takeoff and landing. Accurate forecasting of wind shear events is essential to mitigate these risks and improve both flight safety and operational efficiency. This paper introduces a hybrid Temporal Convolutional Networks and Tree-Based Models (TCNs-TBMs) framework specifically designed for time series modeling and the prediction of wind shear intensity. The framework utilizes the ability of TCNs to capture intricate temporal patterns and integrates it with the predictive strengths of TBMs, such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Categorical Boosting (CatBoost), resulting in robust forecast. To ensure optimal performance, hyperparameter tuning was performed using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), enhancing predictive accuracy. The effectiveness of the framework is validated through comparative analyses with standalone machine learning models such as XGBoost, RF, and CatBoost. The proposed TCN-XGBoost model outperformed these alternatives, achieving a lower Root Mean Squared Error (RMSE: 1.95 for training, 1.97 for testing), Mean Absolute Error (MAE: 1.41 for training, 1.39 for testing), and Mean Absolute Percentage Error (MAPE: 7.90% for training, 7.89% for testing). Furthermore, the uncertainty analysis demonstrated the model’s reliability, with a lower mean uncertainty (7.14 × 10<sup>−8</sup>) and standard deviation of uncertainty (6.48 × 10<sup>−8</sup>) compared to other models. These results highlight the potential of the TCNs-TBMs framework to significantly enhance the accuracy of wind shear intensity predictions, emphasizing the value of advanced time series modeling techniques for risk management and decision-making in the aviation industry. This study highlights the framework’s broader applicability to other meteorological forecasting tasks, contributing to aviation safety worldwide. |
| format | Article |
| id | doaj-art-ed96630454b74de59a45ab964bd5c6bf |
| institution | Kabale University |
| issn | 2073-4433 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-ed96630454b74de59a45ab964bd5c6bf2024-11-26T17:50:33ZengMDPI AGAtmosphere2073-44332024-11-011511136910.3390/atmos15111369A New Frontier in Wind Shear Intensity Forecasting: Stacked Temporal Convolutional Networks and Tree-Based Models FrameworkAfaq Khattak0Jianping Zhang1Pak-wai Chan2Feng Chen3Abdulrazak H. Almaliki4Department of Civil, Structural and Environmental Engineering, Trinity College Dublin, Dublin 2, D02 PN40 Dublin, IrelandSecond Research Institute of Civil Aviation Administration of China, Civil Unmanned Aircraft Traffic Management Key Laboratory of Sichuan Province, Chengdu 610041, ChinaHong Kong Observatory, 134A Nathan Road, Kowloon, Hong Kong 999077, ChinaKey Laboratory of Infrastructure Durability and Operation Safety in Airfield of CAAC, Tongji University, 4800 Cao’an Road, Jiading, Shanghai 201804, ChinaDepartment of Civil Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaWind shear presents a considerable hazard to aviation safety, especially during the critical phases of takeoff and landing. Accurate forecasting of wind shear events is essential to mitigate these risks and improve both flight safety and operational efficiency. This paper introduces a hybrid Temporal Convolutional Networks and Tree-Based Models (TCNs-TBMs) framework specifically designed for time series modeling and the prediction of wind shear intensity. The framework utilizes the ability of TCNs to capture intricate temporal patterns and integrates it with the predictive strengths of TBMs, such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Categorical Boosting (CatBoost), resulting in robust forecast. To ensure optimal performance, hyperparameter tuning was performed using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), enhancing predictive accuracy. The effectiveness of the framework is validated through comparative analyses with standalone machine learning models such as XGBoost, RF, and CatBoost. The proposed TCN-XGBoost model outperformed these alternatives, achieving a lower Root Mean Squared Error (RMSE: 1.95 for training, 1.97 for testing), Mean Absolute Error (MAE: 1.41 for training, 1.39 for testing), and Mean Absolute Percentage Error (MAPE: 7.90% for training, 7.89% for testing). Furthermore, the uncertainty analysis demonstrated the model’s reliability, with a lower mean uncertainty (7.14 × 10<sup>−8</sup>) and standard deviation of uncertainty (6.48 × 10<sup>−8</sup>) compared to other models. These results highlight the potential of the TCNs-TBMs framework to significantly enhance the accuracy of wind shear intensity predictions, emphasizing the value of advanced time series modeling techniques for risk management and decision-making in the aviation industry. This study highlights the framework’s broader applicability to other meteorological forecasting tasks, contributing to aviation safety worldwide.https://www.mdpi.com/2073-4433/15/11/1369wind sheartime series modelingtemporal convolutional networkstree-based models |
| spellingShingle | Afaq Khattak Jianping Zhang Pak-wai Chan Feng Chen Abdulrazak H. Almaliki A New Frontier in Wind Shear Intensity Forecasting: Stacked Temporal Convolutional Networks and Tree-Based Models Framework Atmosphere wind shear time series modeling temporal convolutional networks tree-based models |
| title | A New Frontier in Wind Shear Intensity Forecasting: Stacked Temporal Convolutional Networks and Tree-Based Models Framework |
| title_full | A New Frontier in Wind Shear Intensity Forecasting: Stacked Temporal Convolutional Networks and Tree-Based Models Framework |
| title_fullStr | A New Frontier in Wind Shear Intensity Forecasting: Stacked Temporal Convolutional Networks and Tree-Based Models Framework |
| title_full_unstemmed | A New Frontier in Wind Shear Intensity Forecasting: Stacked Temporal Convolutional Networks and Tree-Based Models Framework |
| title_short | A New Frontier in Wind Shear Intensity Forecasting: Stacked Temporal Convolutional Networks and Tree-Based Models Framework |
| title_sort | new frontier in wind shear intensity forecasting stacked temporal convolutional networks and tree based models framework |
| topic | wind shear time series modeling temporal convolutional networks tree-based models |
| url | https://www.mdpi.com/2073-4433/15/11/1369 |
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