Flight Delay Prediction Model Based on Lightweight Network MobileNetV2

‍ ‍For the increasing prominence of flight delay in civil aviation industry， the traditional algorithms have problems with low accuracy， calculation quantity and large number of parameters. Facing the demand of passengers' main query using mobile devices， traditional algorithms are difficult to be directly deployed on the mobile terminal. A flight delay prediction model based on lightweight network MobileNetV2 is proposed in this paper. Firstly， Datasets are to be fused and encoded in this model. Then， the preprocessed data is input into the network for feature extraction. Finally， it uses the Softmax classifier to output the flight delay level. Applied to domestic datasets， the highest accuracy is 99.07% and the number of parameters of the model is 1.31Million， computation of 40.58Million. While ensuring the accuracy， the present model reduces the parameters and computation of the model， which outperforms the traditional network， and helps to realize the flight delay prediction on the mobile end.