The evaporation duct is a special atmospheric stratification that significantly influences the propagation path of electromagnetic waves at sea, and hence, it is crucial for the stability of the radio communication systems. Affected by physical parameters that are not universal, traditional evaporation duct theoretical models often have limited accuracy and poor generalization ability, e.g., the remote sensing method is limited by the inversion algorithm. The accuracy, generalization ability and scientific interpretability of the existing pure data-driven evaporation duct height prediction models still need to be improved. To address these issues, in this paper, we use the voyage observation data and propose the physically constrained LightGBM evaporation duct height prediction model (LGB-PHY). The proposed model integrates the Babin–Young–Carton (BYC) physical model into a custom loss function. Compared with the eXtreme Gradient Boosting (XGB) model, the LGB-PHY based on a 5-day voyage data set of the South China Sea provides significant improvement where the RMSE index is reduced by 68%, while the SCC index is improved by 6.5%. We further carried out a cross-comparison experiment of regional generalization and show that in the sea area with high latitude and strong adaptability of the BYC model, the LGB-PHY model has a stronger regional generalization performance than that of the XGB model.
Evaporation duct is a kind of special atmospheric stratification that frequently appears on the sea surface, which has an important influence on the propagation and attenuation of electromagnetic waves, and is an important factor affecting the efficiency of marine radars and communication equipment. After the development in more than half a century, evaporation duct height can be obtained by direct detection, theoretical model, inversion and machine learning. Machine learning can explore the hidden laws of data efficiently and has the potential to surpass the traditional theoretical model. In this paper, the Machine Learning methods in evaporation duct research are shown and prospects of machine learning methods in evaporation duct research are given.
The evaporation duct is a special atmospheric stratification that can affect the propagation path of electromagnetic waves at sea, thus it is crucial for the ability of the radio communication systems. The traditional theoretical models of the evaporation duct often have limited accuracy. The actual observational data from voyages and stations are insufficient, and the existing data-driven evaporation duct height prediction models can only predict a particular point or route but cannot reproduce the regional distribution of the evaporation duct. To address these issues, we propose NWPP-EDH model in this work. The fitting ability of the NWPP-EDH model was tested. Its accuracy was compared with that of the Babin–Young–Carton model, Musson–Gauthier–Bruth model, and the classical Naval Postgraduate School model; compared to these models, the root mean square error (RMSE) of NWPP-EDH model was reduced by 71.8%, 87%, and 60.9%, respectively. Thus, we find that the model shows a better performance than the existing theoretical models.
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