An Evaporation Duct Height Prediction Method Based on Deep Learning

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.