通过使用基于机器学习/深度学习算法的预测工具预测起飞航班延误

J. G. Muros Anguita, O. Díaz Olariaga
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引用次数: 0

摘要

本研究的目的是通过使用基于机器学习/深度学习(ML/DL)的预测工具和基于可用飞行数据集的监督回归训练的方法来预测定期商业航班的起飞延误。由于这项工作的新贡献是,首先,根据实现的不同ML/DL模型的平均值和统计方差对预测进行比较,其次,使用称为排列重要性的ML方法确定特征或飞行属性的重要性系数,可以根据飞行属性对延误时间的影响对其重要性进行排序,减少选择最重要的飞行属性的问题。从得到的结果来看,值得一提的是,表现最好的模型是随机森林回归模型的集合或组合方法,具有可接受的预测范围(用均方根误差测量)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of departure flight delays through the use of predictive tools based on machine learning/deep learning algorithms
The objective of this research is to predict the delays in the departure of scheduled commercial flights through a methodology that uses predictive tools based on machine learning/deep learning (ML/DL), with supervised training in regression, based on the available flight datasets. Since the novel contribution of this work is, first, to make the comparison of the predictions in terms of means and statistical variance of the different ML/DL models implemented and, second, to determine the coefficients of the importance of the features or flight attributes, using ML methods known as permutation importance, it is possible to rank the importance of flight attributes by their influence in determining the delay time and reduce the problem of selecting the most important flight attributes. From the results obtained, it is worth mentioning that the model that presents the best performance is the ensemble or combinatorial method of random forest regressor models, with an acceptable prediction range (measured with the root-mean-square-error).
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