Application of Convolutional Neural Networks (CNN) for Optimizing Route Changes Based on Dynamic Weather Conditions and Travel Time

Sunardi Sunardi, Marzuki Sinambela, Syahrul Humaidi, Marhaposan Situmorang

Abstract


This research aims to apply Convolutional Neural Networks (CNN) in optimizing flight route changes by considering dynamic weather conditions and travel time. In the context of air traffic management, fast and precise route changes are critical to reducing flight delays and increasing operational efficiency. The data used includes flight information, current weather conditions and historical travel time data. The research process begins with data collection and preprocessing to ensure the quality and consistency of the information. The CNN model is built with an architecture consisting of several convolution and pooling layers to extract features from input data. The evaluation results show that the CNN model achieves an accuracy of 92% in predicting optimal route changes, which shows better performance compared to traditional algorithms such as support vector machine and random forest. These findings confirm the potential of CNNs in improving air traffic management through route change optimization based on real-time analysis of weather and time data. This research contributes to the development of a more adaptive and responsive system in facing challenges in the aviation industry.

Keywords


Prediction, CNN, Optimization, air traffic, weather.

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