Airline flight delays using artificial intelligence in COVID-19 with perspective analytics
نویسندگان
چکیده
This study envisages assessing the effects of COVID-19 on on-time performance US-airlines industry in disrupted situations. The deep learning techniques used are neural network regression, decision forest boosted tree regression and multi class logistic regression. best technique is identified. In perspective data analytics, it suggested what airlines should do for situation. performances all methods satisfactory. coefficient determination 0.86 0.85, respectively. 0.870984. Thus better. Multi gives an overall accuracy precision 98.4%. Recalling/remembering 99%. model prediction flight delays COVID-19. confusion matrix shows that 87.2% flights actually not delayed predicted delayed. but wrongly are12.7%. strength relation with departure delay, carrier late aircraft weather delay NAS 94%, 53%, 35%, 21%, 14%, There a weak negative (almost unrelated) air time arrival delay. Security also almost unrelated 1% relationship. Based these diagnostic recommended as to take due care reducing Late Nas respectively, considerably effect 14% proposed models have MAE 2% Neural Network Regression, Decision Forest Boosted Tree and, RMSE approximately, 11%, 12%,
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ژورنال
عنوان ژورنال: Journal of Intelligent and Fuzzy Systems
سال: 2023
ISSN: ['1875-8967', '1064-1246']
DOI: https://doi.org/10.3233/jifs-222827