TCN-GRU: A Diagnostic Model for the Evaporation Duct Height

The Evaporation duct (ED) is a special type of atmospheric layer that can trap electromagnetic waves of certain frequencies, enabling over-the-horizon transmission. Accurate diagnosis and prediction of evaporation duct height have a significant impact on the performance of the radar and communication equipment. This paper proposes a hybrid Temporal Convolutional Network-Gated Recurrent Unit (TCN-GRU) EDH diagnostic model that incorporates the height parameters and reconstructs field campaign data distributions by random sampling. It provides a new research perspective and supplements the shortcomings of conventional approaches, which rely solely on time series data. The model’s performance is evaluated through comparative experiments with traditional physical theory models (BYC, MGB, NPS, and LiuLi2.0) and artificial intelligence models (XGB, LSTM) across multiple field campaign datasets. The results demonstrate that the proposed TCN-GRU EDH diagnostic model has improved EDH diagnostic accuracy.