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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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | International Journal of Antennas and Propagation |
| Online Access: | http://dx.doi.org/10.1155/2021/5506599 |
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| author | Yanbo Mai Zheng Sheng Hanqing Shi Qixiang Liao |
| author_facet | Yanbo Mai Zheng Sheng Hanqing Shi Qixiang Liao |
| author_sort | Yanbo Mai |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1f864f5ae47e42d8bed5bcaa71c08799 |
| institution | Kabale University |
| issn | 1687-5869 1687-5877 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Antennas and Propagation |
| spelling | doaj-art-1f864f5ae47e42d8bed5bcaa71c087992025-02-03T07:24:12ZengWileyInternational Journal of Antennas and Propagation1687-58691687-58772021-01-01202110.1155/2021/55065995506599Using Improved XGBoost Algorithm to Obtain Modified Atmospheric Refractive IndexYanbo Mai0Zheng Sheng1Hanqing Shi2Qixiang Liao3College of Electronic Science and Technology, National University of Defense Technology, Changsha 410074, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410074, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410074, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Changsha 410074, ChinaAtmospheric 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.http://dx.doi.org/10.1155/2021/5506599 |
| spellingShingle | Yanbo Mai Zheng Sheng Hanqing Shi Qixiang Liao Using Improved XGBoost Algorithm to Obtain Modified Atmospheric Refractive Index International Journal of Antennas and Propagation |
| title | Using Improved XGBoost Algorithm to Obtain Modified Atmospheric Refractive Index |
| title_full | Using Improved XGBoost Algorithm to Obtain Modified Atmospheric Refractive Index |
| title_fullStr | Using Improved XGBoost Algorithm to Obtain Modified Atmospheric Refractive Index |
| title_full_unstemmed | Using Improved XGBoost Algorithm to Obtain Modified Atmospheric Refractive Index |
| title_short | Using Improved XGBoost Algorithm to Obtain Modified Atmospheric Refractive Index |
| title_sort | using improved xgboost algorithm to obtain modified atmospheric refractive index |
| url | http://dx.doi.org/10.1155/2021/5506599 |
| work_keys_str_mv | AT yanbomai usingimprovedxgboostalgorithmtoobtainmodifiedatmosphericrefractiveindex AT zhengsheng usingimprovedxgboostalgorithmtoobtainmodifiedatmosphericrefractiveindex AT hanqingshi usingimprovedxgboostalgorithmtoobtainmodifiedatmosphericrefractiveindex AT qixiangliao usingimprovedxgboostalgorithmtoobtainmodifiedatmosphericrefractiveindex |