Development of a machine learning model for predicting abnormalities of commercial airplanes  

作　　者：Rossi Passarella Siti Nurmaini Muhammad Naufal Rachmatullah Harumi Veny Fara Nissya Nur Hafidzoh 

机构地区：[1]Department of Computer Engineering,Faculty of Computer Science,Universitas Sriwijaya,30662,Indonesia [2]Intelligent System Research Group(ISysRG),Faculty of Computer Science,Universitas Sriwijaya,30662,Indonesia [3]Department of Informatics,Faculty of Computer Science,Universitas Sriwijaya,30662,Indonesia [4]College of Engineering Studies,Universiti Teknologi MARA(UiTM),Shah Alam,40450,Malaysia

出　　处：《Data Science and Management》2024年第3期256-265,共10页数据科学与管理（英文）

摘　　要：Airplanes are a social necessity for movement of humans,goods,and other.They are generally safe modes of transportation;however,incidents and accidents occasionally occur.To prevent aviation accidents,it is necessary to develop a machine-learning model to detect and predict commercial flights using automatic dependent surveillance–broadcast data.This study combined data-quality detection,anomaly detection,and abnormality-classification-model development.The research methodology involved the following stages:problem statement,data selection and labeling,prediction-model development,deployment,and testing.The data labeling process was based on the rules framed by the international civil aviation organization for commercial,jet-engine flights and validated by expert commercial pilots.The results showed that the best prediction model,the quadratic-discriminant-analysis,was 93%accurate,indicating a“good fit”.Moreover,the model’s area-under-the-curve results for abnormal and normal detection were 0.97 and 0.96,respectively,thus confirming its“good fit”.

关 键 词：Automatic dependent surveillance-broadcast data Commercial airplanes accident Data-labeling Machine learning Prediction model 

分 类 号：F56[经济管理—产业经济]