Developing a useful sustainable model for air traffic using quantum artificial intelligence based on uav and earth data during the process of climate change

of sustainable air traffic modeling during the process of climate change and discusses different methodologies in this regard. The aim of the thesis is to develop a useful sustainable model for air traffic using quantum artificial intelligence based on UAV (Unmanned Aerial Vehicle) and Earth data during climate change. UAV flight data and Earth observation satellite data have been used to test this hypothesis. Quantum computing techniques, classification techniques, and statistical analysis techniques have been used. In the thesis, the classification of UAV flight data has been carried out, and quantum computing methods have been compared with classical methods. The accuracy value in the variable quantum classification method was calculated as 0.59. The analysis of Earth observation satellites spatial data for CO2 emissions for January 2022, in Ankara, Istanbul, and Izmir airports compared. When these data are ranked according to the threshold value of 0.03 mol/m2, Ankara is categorized as a low-emission airport, while Istanbul and Izmir airports are identified as high-emission airports. It can be inferred from the data that there is an intersection between regions with high CO2/NO2 emissions and aircraft routes. Satellite emission can be used for calculating airport emission values or airport investments. Sustainability policies can be correlated with transportation policies from a management perspective. Quantum computers and/or quantum computing techniques can be employed for solving numerous algorithmic and computational problems in air traffic control or climate change examples.