The Spatial Distribution of Atmospheric Water Vapor Based on Analytic Hierarchy Process and Genetic Algorithm

The inversion of water vapor spatial distribution using ground-based global navigation satellite systems is a technique that utilizes the propagation delay of satellite signals in the atmosphere to retrieve atmospheric water vapor information. To further promote the accuracy of the information obtained by this method, a satellite system is designed to solve the spatial distribution of atmospheric water vapor based on chromatography technology and genetic algorithm. Firstly, the accuracy of the empirical air temperature and pressure model to calculate the zenith statics delay is analyzed. To optimize the global weighted average temperature model, a model that considers the decreasing rate of atmospheric weighted average temperature and a model based on the linear relationship between surface heat and weighted average temperature are proposed. The idea of removal interpolation restoration is introduced to achieve regional interpolation of atmospheric precipitable water. Finally, in response to the problem of multiple solutions in the current water vapor chromatography equation, a genetic algorithm based chromatography method is put forward to achieve the solution of atmospheric water vapor spatial distribution. The experimental analysis shows that the average root mean square error and average absolute error of the design method of the research institute are 1.78g/m3 and 1.41g/m3, respectively, which can realize the calculation of atmospheric water vapor density distribution with high accuracy.

@article{Wei2023,
  title     = {The Spatial Distribution of Atmospheric Water Vapor Based on Analytic Hierarchy Process and Genetic Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {8},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Fengjun Wei and Chunhua Liu and Rendong Guo and Xin Li and Jilei Hu and Chuanxun Che},
  doi       = {10.14569/IJACSA.2023.0140807},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140807}
}