Modelling of Atmospheric Parameters Using Artificial Neural Networks

Demirtas O., EFE M. Ö.

9th International Conference on Recent Advances in Space Technologies (RAST), İstanbul, Turkey, 11 - 14 June 2019, pp.571-577 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/rast.2019.8767466
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.571-577
  • Hacettepe University Affiliated: Yes


In this article, atmospheric parameters are modelled by using artificial neural networks and the obtained models are compared with atmospheric lookup tables in terms of accuracy, speedup and memory usage. First, input and output data were generated for the five different atmosphere layers divided by altitude ranges using the U.S. Standard Atmosphere 1976 atmosphere model. Then, the artificial neural networks trained with these data were added to the simulation and measurements were taken. The results show that the use of artificial neural network modelled by using atmospheric data instead of atmospheric lookup table is more efficient and encourages new studies.