Developing a Model Forecasting Extra Fuel for Airbus A320-200 Landing at Suvarnabhumi Airport: a case study of Thai Smile Airways
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Abstract
This research aims to develop a model forecasting the amount of extra fuel of Airbus A320-200 landing at Suvarnabhumi Airport to achieve economy purpose within an acceptable level of flight safety. This research was carried out with mixed methods using quantitative leading qualitative technique by gathering aeronautical variables which was summarized into 8 items derived from captain interviews associated with the secondary data from the 427 historical flight data, divided into 307 training set with 120 testing set. The data were analyzed by Artificial Neural Networks technique to create the Extra Fuel Forecasting Model (EFFM). The backward test forecasting was then performed for the efficiency and expense comparison, along with utilization of the EFFM by pilot in flight. The results found that the accuracy of the EFFM was assessed by the sum of square error, equaled to 0.346. By the relative error, the accuracy value of actual flight test equaled to 91.67%. However, if THAI Smile Airways utilized the EFFM from 1 April 2019 to 31 March 2020, the airline would have reduced expenses by 10,929,197.60 baht. In conclusion, the EFFM is function to save the fuel costs and assure the pilots to operate flight safely.
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References
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