An Evaporation Duct Height Prediction Model Based on a Long Short-Term Memory Neural Network

Abstract

Evaporation ducts are a particular type of atmospheric stratification that frequently appears on the sea surface. The accurate and timely prediction of the evaporation duct height (EDH) is significant for the practical application of electromagnetic communication equipment. Due to the typical time-series (TS) characteristics of the measured evaporation duct data, we construct an EDH prediction model based on a long short-term memory network (LSTM-EDH model) for the first time. The experimental results show that the LSTM-EDH model’s root-mean-square error (RMSE) is dramatically reduced and can achieve a better fit of the measured EDH compared with the Babin–Young–Carton (BYC), Naval-Postgraduate-School (NPS), and eXtreme Gradient Boosting (XGB) EDH models. Compared with the XGB model, the generalization ability is also greatly improved.