@Article{cmes.2025.059914,
AUTHOR = {Afaq Khattak, Pak-wai Chan, Feng Chen, Abdulrazak H. Almaliki},
TITLE = {Deep ResNet Strategy for the Classification of Wind Shear Intensity Near Airport Runway},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {142},
YEAR = {2025},
NUMBER = {2},
PAGES = {1565--1584},
URL = {http://www.techscience.com/CMES/v142n2/59394},
ISSN = {1526-1506},
ABSTRACT = {Intense wind shear (I-WS) near airport runways presents a critical challenge to aviation safety, necessitating accurate and timely classification to mitigate risks during takeoff and landing. This study proposes the application of advanced Residual Network (ResNet) architectures including ResNet34 and ResNet50 for classifying I-WS and Non-Intense Wind Shear (NI-WS) events using Doppler Light Detection and Ranging (LiDAR) data from Hong Kong International Airport (HKIA). Unlike conventional models such as feedforward neural networks (FNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), ResNet provides a distinct advantage in addressing key challenges such as capturing intricate WS dynamics, mitigating vanishing gradient issues in deep architectures, and effectively handling class imbalance when combined with Synthetic Minority Oversampling Technique (SMOTE). The analysis results revealed that ResNet34 outperforms other models with a Balanced Accuracy of 0.7106, Probability of Detection of 0.8271, False Alarm Rate of 0.328, F1-score of 0.7413, Matthews Correlation Coefficient of 0.433, and Geometric Mean of 0.701, demonstrating its effectiveness in classifying I-WS events. The findings of this study not only establish ResNet as a valuable tool in the domain of WS classification but also provide a reliable framework for enhancing operational safety at airports.},
DOI = {10.32604/cmes.2025.059914}
}