Convective Weather Avoidance Prediction in Enroute Airspace Based on Support Vector Machine

基于支持向量机的航路空域内对流天气避让预测

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.

随着全球空中交通的快速发展,航班延误问题越来越严重。对流天气是造成航班延误的主要原因之一,已经影响到民航行业的可持续发展,成为一个社会问题。如果能够提前预测与天气有关的航班是否改航,那么空中交通活动的参与者就可以协调调度,极大减少航班延误。本文提出了天气避让预测模型(Weather avoidance prediction model,WAPM),以寻找天气与飞行轨迹之间的关系,并基于历史飞行数据预测未来航班是否改航。由于天气数据量大,采用主成分分析对10维天气指标进行降维以提取90%的信息。然后通过确定径向基函数的超参数c和γ,利用支持向量机来预测飞行是否改航。最后,通过预测准确率、精度、查全率和F1评价模型性能,并与k近邻(k nearest neighbor,kNN)、逻辑回归(Logistic regression,LR)、随机森林(Random forest,RF)和深度神经网络(Deep neural network,DNN)进行比较。对于准确率,WAPM比kNN、LR、RF和DNN方法分别高5.22%、2.63%、2.26%和1.03%;对于精度,WAPM比kNN、LR、RF和DNN方法分别大6.79%、5.19%、4.37%和3.21%;对于查全率,WAPM比kNN、LR、RF分别大4.05%、1.05%、0.04%,比DNN低1.38%;对于F1,WAPM比kNN、LR、RF和DNN方法分别高5.28%、1.69%、1.98%和0.68%。