Research on evaporation duct height prediction based on back propagation neural network

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.