Weather-based Aviation Delay Predictions
Dr. K Sreekumar1, Varun Iyer2, Karan Matalia3
1Varun Iyer, Department of Computer Science Engineering, SRM Institute of Science & Technology, Chennai, India
2Karan Matalia, Department of Computer Science Engineering, SRM Institute of Science & Technology, Chennai, India.
3Dr. K. Sreekumar, Department of Computer Science Engineering, SRM Institute of Science & Technology, Chennai, India.
Manuscript received on 11 April 2019 | Revised Manuscript received on 19 May 2019 | Manuscript published on 30 May 2019 | PP: 1257-1261 | Volume-8 Issue-1, May 2019 | Retrieval Number: A3208058119/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Airline Industry is one of the major contributors towards socio-economic utilityand forms a vital part of worldwide transportation system. The aviation industry hasevolved immensely over the past couple of decades, however flight delays and cancellations are an inevitable part of the industry thathurts passengers along with the airlines and the airport itself. Given flight delays results in economic and environmental impact, therefore, it becomes absolutely essential to improve the air traffic management. In thispaperwepredictflightdelaysincluding delay due to factors likeinclement weather conditions,precipitation, temperature. Wind speedetc. We prediction models by leveraging the power of data science and machine learning models.It was carried out in two parts – the first being classification and the second using multiple regression techniques and evaluated the best models based on their respective accuracy parameters. Data Scientist have lately found excellent results using Gradient Boosting and hence we intend to utilize it for better accuracy and predictions. Our work could be used by the airliners and the passengers alike for getting an idea about the delays so that they can effectively manage their time and resources beforehand hence minimizing wastage.
Index Terms: Decision Tree, Gradient Boost, Passenger Carrier on-time Performance, Weather, XG Boost
Scope of the Article: Social Sciences