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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. [ABSTRACT FROM AUTHOR]

Evaporation duct is an abnormal refraction atmospheric stratification frequently appearing at sea. Accurately obtaining evaporation duct height (EDH) is significant for the effective application of electromagnetic system equipment. To overcome the limitations of theoretical prediction models, this study proposes a pure data-driven back propagation neural network (BPNN) EDH prediction model (referred to as BPNN model) for the first time, which uses BPNN suitable for the data characteristics of evaporation duct and then conducts two groups of experiments to fully test the proposed BPNN model. The Paulus–Jeske (PJ) model and support vector regression (SVR) model are introduced as the baseline methods in these experiments. The results of the first group of experiments prove that on the EDH prediction in all the experimental areas, the BPNN model has significantly better comprehensive performances than the PJ model and the SVR model. Also, the second group of experiments' results reflects the BPNN model has great generalisation capacity, which makes it suitable for predicting the EDH, and the distributions of EDH have a strong spatial correlation. [ABSTRACT FROM AUTHOR]

An evaporation duct is a particular atmospheric duct that is crucial for marine vessel communication, and one of the most significant indexes to assess it is evaporation duct height (EDH). In this letter, we propose a new method based on a multilayer perceptron (MLP), a classic network in deep learning, to predict the EDH. After so many experiments, the structure of MLP for EDH estimation is chosen as five hidden layer with rectified liner unit activation function. Since this method is essentially some kind of regression, the mean-squared error is chosen as the loss function. To accelerate the training convergence, we choose a self-adaptive optimization scheme called adaptive moment estimation. There are some field observation data sets obtained from some sea areas in northern hemisphere, by which the MLP is trained, to predict the EDH. For single-regional prediction, we use the same input data sets with the Paulus–Jeske (P-J) model, one of the ideal operational models, to predict the EDH. In comparison with the P-J model, the prediction accuracy using our method is significantly escalated in all experimental sea areas, which reveals the efficiency of our method. By cross prediction in distinct sea areas, the consistency between the method and the theory is verified, and this letter yields a new approach of evaporation duct prediction. [ABSTRACT FROM AUTHOR]