Using Improved XGBoost Algorithm to Obtain Modified Atmospheric Refractive Index

Atmospheric refraction is a special meteorological phenomenon mainly caused by gas molecules and aerosol particles in the atmosphere, which can change the propagation direction of electromagnetic waves in the atmospheric environment. Atmospheric refractive index, an index to measure atmospheric refr...

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Veröffentlicht in:International journal of antennas and propagation Jg. 2021; S. 1 - 11
Hauptverfasser: Mai, Yanbo, Sheng, Zheng, Shi, Hanqing, Liao, Qixiang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Hindawi 23.09.2021
John Wiley & Sons, Inc
Wiley
Schlagworte:
Algorithms
Atmosphere
Atmospheric aerosols
Atmospheric refraction
Back propagation networks
Electromagnetic radiation
Global positioning systems
GPS
Humidity
Machine learning
Mathematical analysis
Measuring instruments
Neural networks
Particle swarm optimization
Propagation
Refractivity
Root-mean-square errors
Wave propagation
ISSN:1687-5869, 1687-5877
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Zusammenfassung:Atmospheric refraction is a special meteorological phenomenon mainly caused by gas molecules and aerosol particles in the atmosphere, which can change the propagation direction of electromagnetic waves in the atmospheric environment. Atmospheric refractive index, an index to measure atmospheric refraction, is an important parameter for electromagnetic wave. Given that it is difficult to obtain the atmospheric refractive index of 100 meters (m)–3000°m over the ocean, this paper proposes an improved extreme gradient boosting (XGBoost) algorithm based on comprehensive learning particle swarm optimization (CLPSO) operator to obtain them. Finally, the mean absolute percentage error (MAPE) and root mean-squared error (RMSE) are used as evaluation criteria to compare the prediction results of improved XGBoost algorithm with backpropagation (BP) neural network and traditional XGBoost algorithm. The results show that the MAPE and RMSE of the improved XGBoost algorithm are 39% less than those of BP neural network and 32% less than those of the traditional XGBoost. Besides, the improved XGBoost algorithm has the strongest learning and generalization capability to calculate missing values of atmospheric refractive index among the three algorithms. The results of this paper provide a new method to obtain atmospheric refractive index, which will be of great reference significance to further study the atmospheric refraction.
Bibliographie:ObjectType-Article-1
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ISSN:1687-5869
1687-5877
DOI:10.1155/2021/5506599