Prediction of Flight Delay using Deep Operator Network with Gradient-mayfly Optimisation Algorithm
| dc.contributor.author | Bala Bisandu, Desmond | |
| dc.contributor.author | Moulitsas, Irene | |
| dc.date.accessioned | 2024-06-03T06:46:16Z | |
| dc.date.available | 2024-06-03T06:46:16Z | |
| dc.date.issued | 2024-02-01 08:47 | |
| dc.description.abstract | Data: This folder contains: - Datasets called Jan_2021_ontime.csv and Nov_2021_ontime.csv were used to obtain the results presented in the journal paper. Source code: This folder contains two files having the instructions on how to run the code and a list of library requirements and folders for each of the ML models as named exactly as contained in the paper which implements the proposed Deep Operator Network with Gradient-mayfly Optimisation Algorithm and all the algorithms presented and validated in the journal paper. Output: This folder contains: - Figures called Figure_1_MAE.png, Figure_1_MAPE.png, Figure_1_RMSE.png, Figure_1_MSE.png, Figure_2_MAE.png,Figure_2_MAPE.png, Figure_2_RMSE.png, Figure_2_MSE.png which shows results from the models based on different train/test ratios, The models are: (A)DBN, (B) Gradient Boosting Classifier, (C) Information Gain-SVM, (D) Multi-Agent Approach, (E) DeepLSTM, (F) SSDCA-based Deep LSTM, (G) DeepONet and (H) Proposed GMOA-based DeepOnet.- Figures called Figure 6A.jpg and Figure 6B.jpg which show the EEG signals before applying the preprocessing pipeline, and after applying the preprocessing pipeline, respectively. - Figures called Figure_1_Prediction_Result_Jan_2021.png and Figure_2_Prediction_Result_Nov_2021.png which are the plots of the prediction results from presented in the journal paper. - 8 csv files called 1 MAE.csv, 1 MAPE.csv, 1 RMSE.csv, 1 MSE.csv, 2 MAE.csv, 2 MAPE.csv, 2 RMSE.csv, 2 MSE.csv, which contains the evaluation results produced by all algorithms presented and validated in the journal paper. - 2 csv files called Delay Prediction_Jan_2021_ontime_Figure_1 and Delay Prediction_Nov_2021_ontime_Figure_2 which contains the prediction results produced by all algorithms presented and validated in the journal paper. | |
| dc.description.sponsorship | UK Research and Innovation | |
| dc.identifier.citation | Bisandu, Desmond Bala; Moulitsas, Irene (2024). Prediction of Flight Delay using Deep Operator Network with Gradient-mayfly Optimisation Algorithm. Cranfield Online Research Data (CORD). Software. https://doi.org/10.17862/cranfield.rd.24037881 | |
| dc.identifier.doi | 10.17862/cranfield.rd.24037881 | |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/21790 | |
| dc.publisher | Cranfield University | |
| dc.relation.supplements | https://doi.org/10.1016/j.eswa.2024.123306' | |
| dc.rights | MIT | |
| dc.rights.uri | https://opensource.org/licenses/MIT | |
| dc.subject | Flight Delay Prediction' | |
| dc.subject | 'Box-Cox Transformation' | |
| dc.subject | 'Deep Residual Network' | |
| dc.subject | 'feature fusion strategies' | |
| dc.subject | 'deep operator networks' | |
| dc.title | Prediction of Flight Delay using Deep Operator Network with Gradient-mayfly Optimisation Algorithm | |
| dc.type | Software |
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