Abstract
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.
Highlights
Atmospheric refraction, which is mainly caused by the gas molecules and aerosol particles, is a special phenomenon of atmospheric environment; it can change the propagation route in the atmospheric environment and cause the abnormal propagation of electromagnetic wave
To verify its feasibility for calculating the modified atmospheric refractive index, the mean absolute percentage error (MAPE) and root mean-squared error (RMSE) are used as evaluation criteria to compare the prediction results of the proposed method with backpropagation (BP) neural network and traditional XGBoost algorithm; the results show that the improved XGBoost algorithm significantly improves accuracy and reduces operation time and has stronger learning and generalization capability to calculate missing values of modified atmospheric refractive index than other algorithms. e results of this paper provide a new method to obtain atmospheric refractive index, which will be of great reference significance to avoid the loss of electromagnetic wave propagation in the future
To measure the effect of filling in missing values in the data, 160 profiles were randomly selected for testing, and the rest of the profiles were used for training. e RMSE and MAPE were used as evaluation indices. ey are calculated as follows:
Summary
Atmospheric refraction, which is mainly caused by the gas molecules and aerosol particles, is a special phenomenon of atmospheric environment; it can change the propagation route in the atmospheric environment and cause the abnormal propagation of electromagnetic wave. Extreme gradient boosting (XGBoost) is an integrated model based on a decision tree [36, 37] It is a typical representative of machine learning, which shows a strong generalization force in big data prediction in recent years. E improved XGBoost algorithm is used for learning and training to fill in missing values of modified atmospheric refractive index in the middle layer. E results of this paper provide a new method to obtain atmospheric refractive index, which will be of great reference significance to avoid the loss of electromagnetic wave propagation in the future. Evaporation ducts mainly appear over the ocean, usually below 40 meters in height
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