Prediction of flight delay using deep operator network with gradient-mayfly optimisation algorithm
| dc.contributor.author | Bisandu, Desmond Bala | |
| dc.contributor.author | Moulitsas, Irene | |
| dc.date.accessioned | 2024-02-07T10:25:19Z | |
| dc.date.available | 2024-02-07T10:25:19Z | |
| dc.date.issued | 2024-01-31 | |
| dc.description.abstract | Accurate flight delay prediction is fundamental to establishing an efficient airline business. It is considered one of the most critical intelligent aviation systems components. Recently, flight delay has been a significant cause that deprives airlines of good performance. Hence, airlines must accurately forecast flight delays and comprehend their sources to have excellent passenger experiences, increase income and minimise unwanted revenue loss. In this paper, we developed a novel approach that is an optimisation-driven deep learning model for predicting flight delays by extending a state-of-the-art method, DeepONet. We utilise the Box-Cox transformation for data conversion with a minimal error rate. Also, we employed a deep residual network for the feature fusion before training our model. Furthermore, this research uses flight on-time data for flight delay prediction. To validate our proposed model, we conducted a numerical study using the US Bureau of Transportation of Statistics. Also, we predict the flight delay by selecting the optimum weights using the novel DeepONet with the Gradient Mayfly Optimisation Algorithm (GMOA). Our experiment results show that the proposed GMOA-based DeepONet outperformed the existing methods with a Root Mean Square Error of 0.0765, Mean Square Error of 0.0058, Mean Absolute Error of 0.0049 and Mean Absolute Percent Error of 0.0043, respectively. When we apply 4-fold cross-validation, the proposed GMOA-based DeepONet outperformed the existing methods with minimal standard error. These results also show the importance of optimisation algorithms in deciding the optimal weight to improve the model performance. The efficacy of our proposed approach in predicting flight delays with minimal errors well define from all the evaluation metrics. Also, utilising the prediction outcome of our robust model to release information about the delayed flight in advance from the aviation decision systems can effectively alleviate the passengers’ nervousness. | en_UK |
| dc.description.sponsorship | UKRI for the COVID-19 recovery grant under the budget code SA077N. This research was heavily affected by the COVID-19 pandemic during the first authors' PhD studies. This lead to an extension to registration for 3 months, which was funded by the UKRI doctoral extension recovery grant. (PTDF main funder of PhD). | en_UK |
| dc.identifier.citation | Bisandu DB, Moulitsas I. (2024) Prediction of flight delay using deep operator network with gradient-mayfly optimisation algorithm. Expert Systems with Applications, Volume 247, August 2024, Article number 123306 | en_UK |
| dc.identifier.eissn | 1873-6793 | |
| dc.identifier.issn | 0957-4174 | |
| dc.identifier.uri | https://doi.org/10.1016/j.eswa.2024.123306 | |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/20756 | |
| dc.language.iso | en | en_UK |
| dc.publisher | Elsevier | en_UK |
| dc.rights | Attribution 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | Flight delay prediction | en_UK |
| dc.subject | Box-cox transformation | en_UK |
| dc.subject | Deep residual network | en_UK |
| dc.subject | Feature fusion | en_UK |
| dc.subject | Deep operator network | en_UK |
| dc.title | Prediction of flight delay using deep operator network with gradient-mayfly optimisation algorithm | en_UK |
| dc.type | Article | en_UK |
| dcterms.dateAccepted | 2024-01-20 |