Transactions of Nanjing University of Aeronautics & Astronautics
Convective Weather Avoidance Prediction in Enroute Airspace Based on Support Vector Machine
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Abstract:
With the rapid growth of global air traffic， flight delays are increasingly serious. Convective weather is one of the influential causes for flight delays， which has affected the sustainable development of civil aviation industry and became a social problem. If it can be predicted that whether a weather-related flight diverts， participants in air traffic activities can coordinate the scheduling， and flight delays can be reduced greatly. In this paper， the weather avoidance prediction model （WAPM） is proposed to find the relationship between weather and flight trajectories， and predict whether a future flight diverts based on historical flight data. First， given the large amount of weather data， the principal component analysis is used to reduce the ten dimensional weather indicators to extract 90% information. Second， the support vector machine is adopted to predict whether the flight diverts by determining the hyperparameters c and γ of the radial basis function. Finally， the performance of the proposed model is evaluated by prediction accuracy， precision， recall and F1， and compared with the methods of the k nearest neighbor （kNN）， the logistic regression（LR）， the random forest（RF） and the deep neural networks （DNNs）. WAPM’s accuracy is 5.22%， 2.63%， 2.26% and 1.03% greater than those of kNN， LR， RF and DNNs， respectively； WAPM’s precision is 6.79%， 5.19%， 4.37% and 3.21% greater than those of kNN， LR， RF and DNNs， respectively； WAPM’s recall is 4.05%， 1.05%， 0.04% greater than those of kNN， LR， and RF， respectively， and 1.38% lower than that of the DNNs； and F1 of WAPM is 5.28%， 1.69%， 1.98% and 0.68% greater than those of kNN， LR， RF and DNNs， respectively.
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Project Supported:
This work was supported by Nanjing University of Aeronautics and Astronautics Graduate Innovation Base （Laboratory） Open Fund （No. kfjj20200710）.
Clc Number:
V355
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