PDD_GBR: Research on Evaporation Duct Height Prediction Based on Gradient Boosting Regression Algorithm

摘要

Evaporation duct is a special atmospheric stratification that always exists at sea, which has an important influence on electromagnetic wave propagation. Accurate prediction of evaporation duct height is of great significance for radio information system. In the era of big data, machine learning has been widely used in various research work and achieved a series of excellent results. Based on the observation data method, this paper studies the prediction model using Gradient Boosting Regression algorithm (GBR) and proposes the pure data-driven GBR (PDD_GBR), GBR_Paulus-Jeske (GBR_PJ), and GBR_Babin-Young-Carton (GBR_BYC) evaporation duct prediction models. Simultaneously, traditional Paulus-Jeske (PJ) model, Babin-Young-Carton (BYC) model, and existing SVR_PJ model are introduced into the experiment to make a comparison. The comprehensive performance of PDD_GBR model is optimal among these models with high stability and strong generalization ability, whose prediction accuracy has a great promotion compared with other models.