Weather-induced flight delay prediction using Machine Learning
| dc.contributor.author | Zhumagali Ye. | |
| dc.date.accessioned | 2024-12-20T04:48:08Z | |
| dc.date.available | 2024-12-20T04:48:08Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Many businesses depend on different airlines to link them to other parts of the world, and the aviation industry today plays a crucial role in the global transportation sector. Extreme weather, on the other hand, might cause flight delays, which can have a direct influence on airline services. To solve this issue, accurately projecting flight delays enables passengers to be well prepared for their journey’s interruption and allows airlines to react to anticipated causes of flight delays ahead of time, decreasing the variety of consequences. As a consequence, airlines and experts are focusing their efforts on cutting down on flight delays. It is vital to predict flight delays in order to reduce aircraft delays. Establishing a reliable and accurate flight delay prediction system may provide decision-makers with a clear path to make effective scheduling choices. Weather is the most common reason for aircraft delays, and subsequently it is linked to other categories. The NaS type, for example, might include delays caused by fly rerouting due to bad weather. Late-arriving aircraft is also influenced by weather, but airlines do not define the reasons as weather. When these factors are taken into consideration, over 40% of late minutes are accounted for by this factor. as a result, assessing the impact of bad weather on carrier delays is critical for smooth flight operations. | |
| dc.identifier.uri | https://repository.sdu.edu.kz/handle/123456789/1598 | |
| dc.language.iso | en | |
| dc.subject | aviation, global transportation, airlines, delays, flight operations | |
| dc.title | Weather-induced flight delay prediction using Machine Learning | |
| dc.type | Other |