We develop a memory graph convolutional network (MGCN) framework for sea surface temperature (SST) prediction. The MGCN consists of two memory layers: one graph layer and one …
Accurately predicting wave height has a significant impact on offshore production and marine transportation. Numerical model predictions are the most commonly used wave height …
This paper investigates the possibility of using machine learning technology to correct wave height series numerical predictions. This is done by incorporating numerical …
Estimating the ocean subsurface thermal structure (OSTS) based on multisource sea surface data in the western Pacific Ocean is of great significance for studying ocean dynamics …
Orthogonal matching pursuit (OMP) is an efficient method for decomposing a seismic trace with regard to an atom dictionary. The original OMP optimizes one unique single objective …
Highlights•We propose a new sea surface temperature inversion model.•The model is based on deep neural network.•This model is more accurate than traditional method.•The model is …
Using the data of three M eteorological Stations at Fushan, Jiʼnan and Juxian, 2000-2003, the Elman neural network model was established to estimate the daily solar irradiance. The …
Because fog is a high-impact weather phenomenon, there has been increased demand for its accurate prediction. Both surface data and wind profiler data possess great potential for …
