Abstract
Evaporation duct is a kind of chaotic phenomenon over the ocean. In this paper, a new nonlinear prediction algorithm, the Darwinian evolutionary algorithm (DEA), is introduced to obtain the specific nonlinear formula $P(\cdot)$ of the chaotic phenomenon. Based on Darwinian natural selection and survival theory, the method first selects a suitable training set of samples, and then produces an initial population before going through an evolutionary process of selection, reproduction and mutation until the optimal individual is found. Finally, a specific expression for a nonlinear chaotic time series is obtained, which can realize the short-term prediction of evaporation duct height (EDH) quickly and accurately. After that, the DEA, the support vector regression (SVR), and the back propagation (BP) neural network were applied to predict the EDH which were formed over the ocean by using sounding data. After interpolation and smoothing of the original data, we selected the first 250 data as training samples and the last 115 data as test samples to test the effect of the EDA algorithm. The results showed that the root mean squared error (RMSE) for the DEA was about 7% less than that of the SVR and 10% less than that of BP neural network; the mean absolute percent error (MAPE) for the DEA was about 9% less than that of the SVR and 15% less than that of BP neural network. In addition, the DEA obtained, for the first time, a nonlinear expression for EDH, which provides an important reference for future research on the evaporation ducts.
Highlights
Evaporation ducts are a type of near surface duct in the marine environment formed by the evaporation of sea water; due to subjecting to turbulence, weather and other meteorological factors, it is a typical chaotic phenomenon
The formation mechanism of the ducts is due to the unbalanced thermal structures existing between the atmosphere and the ocean boundaries, which lead to sea-air interactions and cause the evaporation of water vapor from the sea surface, such that a large amount of water vapor may reside at the sea surface
That of the support vector regression (SVR) and back propagation (BP) neural network was 2.3286 and 2.5166 respectively, compared to 2.1294 for the Darwinian evolutionary algorithm (DEA); the value for the DEA was about 9% less than that for the SVR and 15% less than that of BP neural network. These results demonstrated that the overall prediction accuracy of the DEA was better than that of the SVR and BP neural network
Summary
Evaporation ducts are a type of near surface duct in the marine environment formed by the evaporation of sea water; due to subjecting to turbulence, weather and other meteorological factors, it is a typical chaotic phenomenon. Given that evaporation ducts have a great influence on radar navigation and communication systems, it is important to effectively avoid the negative influences of the evaporation ducts on the radar system This becomes possible if we can predict the EDH in advance. Some scholars use different machine learning algorithms to predict the EDH [25], [26] Application of such tools in the study of evaporation ducts would allow us to obtain the EDH information in advance, and effectively avoid the negative effects of evaporation ducts on electromagnetic wave transmission. VOLUME 8, 2020 the negative effects of the ducts on electromagnetic wave propagation
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